Step 1: Investigating the dataset¶

In [1375]:
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
In [1701]:
data = pd.read_csv('daily_engagement_full.csv')
In [1703]:
data.head()
Out[1703]:
utc_date acct registration_date subscription_start course_key sibling_key course_title has_visited total_minutes_visited lessons_completed projects_completed account_key
0 2014-11-05 448 2014-08-05 2014-11-05 ud359-nd ud359 Intro to Data Science 0.0 0.0 0.0 0.0 2257038596
1 2014-11-05 448 2014-08-05 2014-11-05 ud120-nd ud120 Intro to Machine Learning 0.0 0.0 0.0 0.0 2257038596
2 2014-11-05 448 2014-08-05 2014-11-05 ud651-nd ud651 Data Analysis with R 0.0 0.0 0.0 0.0 2257038596
3 2014-11-05 448 2014-08-05 2014-11-05 ud507-nd ud507 Data Visualization and D3.js 0.0 0.0 0.0 0.0 2257038596
4 2014-11-05 448 2014-08-05 2014-11-05 ud651 ud651 Data Analysis with R 0.0 0.0 0.0 0.0 2257038596
In [1378]:
data.columns
Out[1378]:
Index(['utc_date', 'acct', 'registration_date', 'subscription_start',
       'course_key', 'sibling_key', 'course_title', 'has_visited',
       'total_minutes_visited', 'lessons_completed', 'projects_completed',
       'account_key'],
      dtype='object')
In [1379]:
print('Shape of the Dataframe:',data.shape,'\n')
Shape of the Dataframe: (2309239, 12) 

In [1380]:
data.dtypes
Out[1380]:
utc_date                  object
acct                       int64
registration_date         object
subscription_start        object
course_key                object
sibling_key               object
course_title              object
has_visited              float64
total_minutes_visited    float64
lessons_completed        float64
projects_completed       float64
account_key               object
dtype: object
In [1382]:
%%time
start='\033[1m'
end='\033[0;0m'
Wall time: 0 ns
In [1383]:
all_null = data.isnull().sum().sum()
not_null = data.notnull().sum().sum()
print(f"{start}Null values number for all table is {all_null}, while not null values count is {not_null}{end}") 
Null values number for all table is 0, while not null values count is 27710868
In [1384]:
data.isnull().sum()
Out[1384]:
utc_date                 0
acct                     0
registration_date        0
subscription_start       0
course_key               0
sibling_key              0
course_title             0
has_visited              0
total_minutes_visited    0
lessons_completed        0
projects_completed       0
account_key              0
dtype: int64
In [1385]:
print(f'{start}Summary Of the dataframe:{end}','\n')
print(data.info(),'\n')
Summary Of the dataframe: 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2309239 entries, 0 to 2309238
Data columns (total 12 columns):
 #   Column                 Dtype  
---  ------                 -----  
 0   utc_date               object 
 1   acct                   int64  
 2   registration_date      object 
 3   subscription_start     object 
 4   course_key             object 
 5   sibling_key            object 
 6   course_title           object 
 7   has_visited            float64
 8   total_minutes_visited  float64
 9   lessons_completed      float64
 10  projects_completed     float64
 11  account_key            object 
dtypes: float64(4), int64(1), object(7)
memory usage: 211.4+ MB
None 

In [1387]:
print(f'{start}Describtion Of the dataframe:{end}','\n')
data.describe()
Describtion Of the dataframe: 

Out[1387]:
acct has_visited total_minutes_visited lessons_completed projects_completed
count 2.309239e+06 2.309239e+06 2.309239e+06 2.309239e+06 2.309239e+06
mean 4.496441e+02 2.012178e-02 1.448495e+00 7.598607e-03 3.971005e-04
std 3.215611e+02 1.404169e-01 1.525523e+01 1.128178e-01 1.992343e-02
min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
25% 2.010000e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
50% 3.940000e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
75% 5.960000e+02 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00
max 1.305000e+03 1.000000e+00 7.371061e+02 1.600000e+01 1.000000e+00
In [1412]:
describ_cols = data.describe().columns
In [1433]:
describ_cols
Out[1433]:
Index(['acct', 'has_visited', 'total_minutes_visited', 'lessons_completed',
       'projects_completed'],
      dtype='object')
In [1391]:
data_describtion = data.describe().transpose()
In [1392]:
data_describtion
Out[1392]:
count mean std min 25% 50% 75% max
acct 2309239.0 449.644054 321.561123 0.0 201.0 394.0 596.0 1305.000000
has_visited 2309239.0 0.020122 0.140417 0.0 0.0 0.0 0.0 1.000000
total_minutes_visited 2309239.0 1.448495 15.255230 0.0 0.0 0.0 0.0 737.106143
lessons_completed 2309239.0 0.007599 0.112818 0.0 0.0 0.0 0.0 16.000000
projects_completed 2309239.0 0.000397 0.019923 0.0 0.0 0.0 0.0 1.000000
In [1411]:
data_describtion.columns
Out[1411]:
Index(['count', 'mean', 'std', 'min', '25%', '50%', '75%', 'max'], dtype='object')
In [1421]:
data_describtion.dtypes
Out[1421]:
count    float64
mean     float64
std      float64
min      float64
25%      float64
50%      float64
75%      float64
max      float64
dtype: object
In [ ]:
 
In [1434]:
def data_skewness(df, mean, median, lst_of_cols):
    for i, m in (enumerate(df[mean])):
        for j, med in (enumerate(df[median])):
            if i == j:
                if int(m) > int(med):
                    print('Mean > Median')
                    print(f'{lst_of_cols[i]}: rightly skewed\n\n')
                
                elif int(m) < int(med):
                    print('Mean < Median')
                    print(f'{lst_of_cols[i]}: leftly skewed\n\n')
                
                elif int(m) == int(med):
                    print('Mean ~ Median')
                    print(f'{lst_of_cols[i]}: almost Normal Distributed\n\n')
In [1435]:
data_skewness(data_describtion, 'mean', '50%', describ_cols)
Mean > Median
acct: rightly skewed


Mean ~ Median
has_visited: almost Normal Distributed


Mean > Median
total_minutes_visited: rightly skewed


Mean ~ Median
lessons_completed: almost Normal Distributed


Mean ~ Median
projects_completed: almost Normal Distributed


In [1702]:
def box_plots(df, cols_lst):
    # boxplot all the numerical columns and see if there any outliers
    for i in cols_lst:
        df.iloc[:, 1:].boxplot(column=i)
        plt.title(f"Box Plot Of {i}" , fontsize=20, color="orange")
        plt.savefig(f'{i}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
        plt.show()
In [1704]:
box_plots(data, describ_cols)
In [1709]:
def count_plot(df, cols_lst):
    for i in cols_lst:
        plt.figure(figsize=(12,6))
        plt.xticks(rotation = 45)
        plt.title(f"Analysis of {i}" , fontsize=20, color="orange")
        plt.savefig(f'Analysis of {i}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
        sns.countplot(df[i], color='green')
        plt.show()
In [1710]:
count_plot(data, describ_cols)
D:\Programs\Anaconda2\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning
D:\Programs\Anaconda2\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning
D:\Programs\Anaconda2\lib\site-packages\seaborn\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.
  FutureWarning
---------------------------------------------------------------------------
KeyboardInterrupt                         Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_11492\634224666.py in <module>
----> 1 count_plot(data, describ_cols)

~\AppData\Local\Temp\ipykernel_11492\4205224597.py in count_plot(df, cols_lst)
      5         plt.title(f"Analysis of {i}" , fontsize=20, color="orange")
      6         plt.savefig(f'Analysis of {i}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
----> 7         sns.countplot(df[i], color='green')
      8         plt.show()

D:\Programs\Anaconda2\lib\site-packages\seaborn\_decorators.py in inner_f(*args, **kwargs)
     44             )
     45         kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46         return f(**kwargs)
     47     return inner_f
     48 

D:\Programs\Anaconda2\lib\site-packages\seaborn\categorical.py in countplot(x, y, hue, data, order, hue_order, orient, color, palette, saturation, dodge, ax, **kwargs)
   3608         ax = plt.gca()
   3609 
-> 3610     plotter.plot(ax, kwargs)
   3611     return ax
   3612 

D:\Programs\Anaconda2\lib\site-packages\seaborn\categorical.py in plot(self, ax, bar_kws)
   1637     def plot(self, ax, bar_kws):
   1638         """Make the plot."""
-> 1639         self.draw_bars(ax, bar_kws)
   1640         self.annotate_axes(ax)
   1641         if self.orient == "h":

D:\Programs\Anaconda2\lib\site-packages\seaborn\categorical.py in draw_bars(self, ax, kws)
   1603             # Draw the bars
   1604             barfunc(barpos, self.statistic, self.width,
-> 1605                     color=self.colors, align="center", **kws)
   1606 
   1607             # Draw the confidence intervals

D:\Programs\Anaconda2\lib\site-packages\matplotlib\__init__.py in inner(ax, data, *args, **kwargs)
   1410     def inner(ax, *args, data=None, **kwargs):
   1411         if data is None:
-> 1412             return func(ax, *map(sanitize_sequence, args), **kwargs)
   1413 
   1414         bound = new_sig.bind(ax, *args, **kwargs)

D:\Programs\Anaconda2\lib\site-packages\matplotlib\axes\_axes.py in bar(self, x, height, width, bottom, align, **kwargs)
   2407             elif orientation == 'horizontal':
   2408                 r.sticky_edges.x.append(l)
-> 2409             self.add_patch(r)
   2410             patches.append(r)
   2411 

D:\Programs\Anaconda2\lib\site-packages\matplotlib\axes\_base.py in add_patch(self, p)
   2356         if p.get_clip_path() is None:
   2357             p.set_clip_path(self.patch)
-> 2358         self._update_patch_limits(p)
   2359         self._children.append(p)
   2360         p._remove_method = self._children.remove

D:\Programs\Anaconda2\lib\site-packages\matplotlib\axes\_base.py in _update_patch_limits(self, patch)
   2391         trf_to_data = patch_trf - self.transData
   2392         xys = trf_to_data.transform(vertices)
-> 2393         self.update_datalim(xys, updatex=updatex, updatey=updatey)
   2394 
   2395     def add_table(self, tab):

D:\Programs\Anaconda2\lib\site-packages\matplotlib\axes\_base.py in update_datalim(self, xys, updatex, updatey)
   2477             return
   2478         self.dataLim.update_from_data_xy(xys, self.ignore_existing_data_limits,
-> 2479                                          updatex=updatex, updatey=updatey)
   2480         self.ignore_existing_data_limits = False
   2481 

D:\Programs\Anaconda2\lib\site-packages\matplotlib\transforms.py in update_from_data_xy(self, xy, ignore, updatex, updatey)
    967         path = Path(xy)
    968         self.update_from_path(path, ignore=ignore,
--> 969                               updatex=updatex, updatey=updatey)
    970 
    971     @BboxBase.x0.setter

D:\Programs\Anaconda2\lib\site-packages\matplotlib\transforms.py in update_from_path(self, path, ignore, updatex, updatey)
    900                 self._minpos[0] = minpos[0]
    901             if updatey:
--> 902                 self._points[:, 1] = points[:, 1]
    903                 self._minpos[1] = minpos[1]
    904 

KeyboardInterrupt: 
In [106]:
print(data['acct'].unique())
print(len(data['acct'].unique()))
[448  44 258 ... 874 754 854]
1237
In [107]:
print(data['account_key'].unique())
print(len(data['account_key'].unique()))
['2257038596' '59503937' '1603920644' ... '4804719296' '3567109205'
 'u26334020']
1237
In [108]:
print(f"The number of students: {len(data['account_key'].unique())}")
The number of students: 1237
In [109]:
for col in data.columns:
    #print(f'unique_vals: {data[col].unique()}')
    print(f'column_name: {col}, \n\tnum_of_unique_vals: {len(data[col].unique())}, \n{data[col].value_counts()}', end='\n\n')
column_name: utc_date, 
	num_of_unique_vals: 295, 
2015-07-10    22860
2015-07-11    22752
2015-07-09    22716
2015-08-23    12369
2015-08-25    12274
              ...  
2014-11-08       15
2014-11-07       15
2014-11-06       15
2014-11-09       15
2014-11-05       15
Name: utc_date, Length: 295, dtype: int64

column_name: acct, 
	num_of_unique_vals: 1237, 
1244    7098
442     6518
448     4858
604     4783
568     4783
        ... 
1301      15
1043      15
890       15
908       15
1047      15
Name: acct, Length: 1237, dtype: int64

column_name: registration_date, 
	num_of_unique_vals: 687, 
2014-11-13    66784
2014-12-05    53848
2014-10-07    50461
2014-11-12    36944
2014-11-10    30960
              ...  
2014-10-25       18
2014-12-30       17
2014-06-23       15
2012-02-01       15
2012-02-21       15
Name: registration_date, Length: 687, dtype: int64

column_name: subscription_start, 
	num_of_unique_vals: 115, 
2014-11-10    302388
2015-03-10    147346
2014-11-14     81253
2015-04-08     80870
2015-04-01     80297
               ...  
2015-08-16       266
2015-08-20       228
2014-12-29       210
2015-08-24        95
2014-11-05        75
Name: subscription_start, Length: 115, dtype: int64

column_name: course_key, 
	num_of_unique_vals: 19, 
ud359-nd     138418
ud120        138418
nd002        138418
ud804        138418
ud304        138418
ud507        138418
ud032        138418
ud120-nd     138418
ud359        138418
ud304-nd     138418
ud032-nd     138418
ud804-nd     138418
ud651        138418
ud507-nd     138418
ud651-nd     138418
ud257-nd      89053
ud257         89053
ud134-nd      36308
ud134a-nd     18555
Name: course_key, dtype: int64

column_name: sibling_key, 
	num_of_unique_vals: 11, 
ud359     276836
ud120     276836
ud651     276836
ud507     276836
ud804     276836
ud032     276836
ud304     276836
ud257     178106
nd002     138418
ud134      36308
ud134a     18555
Name: sibling_key, dtype: int64

column_name: course_title, 
	num_of_unique_vals: 11, 
Intro to Data Science           276836
Intro to Machine Learning       276836
Data Analysis with R            276836
Data Visualization and D3.js    276836
JavaScript Basics               276836
Data Wrangling with MongoDB     276836
Intro to HTML and CSS           276836
A/B Testing                     178106
Data Analyst Nanodegree         138418
ud134-nd                         36308
ud134a-nd                        18555
Name: course_title, dtype: int64

column_name: has_visited, 
	num_of_unique_vals: 2, 
0.0    2262773
1.0      46466
Name: has_visited, dtype: int64

column_name: total_minutes_visited, 
	num_of_unique_vals: 45844, 
0.000000     2262773
9.252010           2
91.294271          2
78.330007          2
44.523067          2
              ...   
9.069478           1
68.674106          1
6.371004           1
87.959215          1
42.222797          1
Name: total_minutes_visited, Length: 45844, dtype: int64

column_name: lessons_completed, 
	num_of_unique_vals: 12, 
0.0     2295819
1.0       10435
2.0        2238
3.0         513
4.0         145
5.0          50
6.0          25
7.0           6
8.0           5
16.0          1
10.0          1
9.0           1
Name: lessons_completed, dtype: int64

column_name: projects_completed, 
	num_of_unique_vals: 2, 
0.0    2308322
1.0        917
Name: projects_completed, dtype: int64

column_name: account_key, 
	num_of_unique_vals: 1237, 
993208778     7098
u49383157     6518
2257038596    4858
u42308719     4783
2773048546    4783
              ... 
4140598645      15
u141065         15
1404998541      15
3367788599      15
1604718744      15
Name: account_key, Length: 1237, dtype: int64

In [110]:
dict_col1 = dict(data['acct'].value_counts())
In [111]:
len(dict_col1)
Out[111]:
1237
In [112]:
data1 = pd.DataFrame.from_dict(dict_col1, orient='index').T
data1
Out[112]:
1244 442 448 604 568 3 198 223 140 195 ... 1083 838 994 683 925 1301 1043 890 908 1047
0 7098 6518 4858 4783 4783 4783 4783 4783 4783 4783 ... 15 15 15 15 15 15 15 15 15 15

1 rows × 1237 columns

In [1436]:
def plotData_subplot(df, sub_title, col1, col2):
    fig, axes = plt.subplots(1, 2, figsize=(11, 6))
    fig.suptitle(sub_title, fontsize=16)
    
    dict_col1 = dict(df[col1].value_counts())
    dict_col2 = dict(df[col2].value_counts())
    
    data1 = pd.DataFrame.from_dict(dict_col1, orient='index').T
    data2 = pd.DataFrame.from_dict(dict_col2, orient='index').T
    
    data1.plot(kind='bar', ax=axes[0])
    data2.plot(kind='bar', ax=axes[1])
    
    axes[0].set_title(col1)
    axes[1].set_title(col2)
    plt.show()
In [1437]:
plotData_subplot(data, 'acct vs account_key','acct', 'account_key')
In [1438]:
def plotData(df, col):
    dict_col = dict(df[col].value_counts())
    data = pd.DataFrame.from_dict(dict_col, orient='index').T
    data.plot(kind='bar')
    
    plt.xlabel(col)
    plt.show()
In [1439]:
plotData(data, 'acct')
In [1440]:
plotData(data, 'account_key')

it seems that both columns 'acct' and 'account_key' represent the same data so let's build a function to check that¶

In [1441]:
data.duplicated().sum()
Out[1441]:
34164
In [1442]:
(~data.duplicated()).sum()
Out[1442]:
2275075
In [1443]:
# Extract duplicate rows
data.loc[data.duplicated(), :]
Out[1443]:
utc_date acct registration_date subscription_start course_key sibling_key course_title has_visited total_minutes_visited lessons_completed projects_completed account_key
3911 2015-07-09 448 2014-08-05 2015-03-10 ud359-nd ud359 Intro to Data Science 0.0 0.000000 0.0 0.0 2257038596
3912 2015-07-09 448 2014-08-05 2015-03-10 ud651-nd ud651 Data Analysis with R 0.0 0.000000 0.0 0.0 2257038596
3913 2015-07-09 448 2014-08-05 2015-03-10 ud032-nd ud032 Data Wrangling with MongoDB 0.0 0.000000 0.0 0.0 2257038596
3914 2015-07-09 448 2014-08-05 2015-03-10 ud120-nd ud120 Intro to Machine Learning 0.0 0.000000 0.0 0.0 2257038596
3915 2015-07-09 448 2014-08-05 2015-03-10 ud507-nd ud507 Data Visualization and D3.js 0.0 0.000000 0.0 0.0 2257038596
... ... ... ... ... ... ... ... ... ... ... ... ...
2260954 2015-07-11 813 2015-06-19 2015-07-11 ud032 ud032 Data Wrangling with MongoDB 0.0 0.000000 0.0 0.0 4495968580
2260955 2015-07-11 813 2015-06-19 2015-07-11 ud507 ud507 Data Visualization and D3.js 0.0 0.000000 0.0 0.0 4495968580
2260956 2015-07-11 813 2015-06-19 2015-07-11 ud304 ud304 Intro to HTML and CSS 0.0 0.000000 0.0 0.0 4495968580
2260957 2015-07-11 813 2015-06-19 2015-07-11 ud651 ud651 Data Analysis with R 0.0 0.000000 0.0 0.0 4495968580
2260958 2015-07-11 813 2015-06-19 2015-07-11 nd002 nd002 Data Analyst Nanodegree 1.0 52.856401 0.0 0.0 4495968580

34164 rows × 12 columns

In [1444]:
data.drop_duplicates(inplace=True)
In [1445]:
data.shape
Out[1445]:
(2275075, 12)

Step 2: Preprocessing¶

In [123]:
#def compare_vals(df, col1, col2):
    
In [1446]:
# take two columns and returns true if each colum represent the same relation that the other column represents 
def represent_same(df, col1, col2):
    dict_col1 = dict(df[col1].value_counts())
    dict_col2 = dict(df[col2].value_counts())

    check = [True for i, x in enumerate(dict_col1) for j, y in enumerate(dict_col2) if i==j and dict_col1[x]==dict_col2[y]]

    check = set(check)
    if len(check)==1:
        return True
    return False
In [1447]:
same = represent_same(data, 'acct', 'account_key')
In [1448]:
print(same)
True
So we can dispense with one of the two columns: 'acct' and 'account_key', I prefer to drop 'account key' as it has long numbers, and I'll rename 'acct' into 'account_key'¶
In [1449]:
data.drop('account_key', inplace=True, axis=1)
In [1450]:
data.rename(columns={'acct':'account_key'}, inplace=True)
In [1451]:
data
Out[1451]:
utc_date account_key registration_date subscription_start course_key sibling_key course_title has_visited total_minutes_visited lessons_completed projects_completed
0 2014-11-05 448 2014-08-05 2014-11-05 ud359-nd ud359 Intro to Data Science 0.0 0.0 0.0 0.0
1 2014-11-05 448 2014-08-05 2014-11-05 ud120-nd ud120 Intro to Machine Learning 0.0 0.0 0.0 0.0
2 2014-11-05 448 2014-08-05 2014-11-05 ud651-nd ud651 Data Analysis with R 0.0 0.0 0.0 0.0
3 2014-11-05 448 2014-08-05 2014-11-05 ud507-nd ud507 Data Visualization and D3.js 0.0 0.0 0.0 0.0
4 2014-11-05 448 2014-08-05 2014-11-05 ud651 ud651 Data Analysis with R 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ...
2309234 2015-08-26 854 2012-10-12 2015-08-23 ud359 ud359 Intro to Data Science 0.0 0.0 0.0 0.0
2309235 2015-08-26 854 2012-10-12 2015-08-23 ud804 ud804 JavaScript Basics 0.0 0.0 0.0 0.0
2309236 2015-08-26 854 2012-10-12 2015-08-23 ud651 ud651 Data Analysis with R 0.0 0.0 0.0 0.0
2309237 2015-08-26 854 2012-10-12 2015-08-23 ud304 ud304 Intro to HTML and CSS 0.0 0.0 0.0 0.0
2309238 2015-08-26 854 2012-10-12 2015-08-23 nd002 nd002 Data Analyst Nanodegree 0.0 0.0 0.0 0.0

2275075 rows × 11 columns

In [1452]:
data.dtypes
Out[1452]:
utc_date                  object
account_key                int64
registration_date         object
subscription_start        object
course_key                object
sibling_key               object
course_title              object
has_visited              float64
total_minutes_visited    float64
lessons_completed        float64
projects_completed       float64
dtype: object
In [1453]:
represent_same(data, 'utc_date', 'registration_date')
Out[1453]:
False
In [1454]:
represent_same(data, 'utc_date', 'subscription_start')
Out[1454]:
False
In [1455]:
represent_same(data, 'registration_date', 'subscription_start')
Out[1455]:
False
In [1456]:
data['utc_date'] = pd.to_datetime(data['utc_date'])
data['registration_date'] = pd.to_datetime(data['registration_date'])
data['subscription_start'] = pd.to_datetime(data['subscription_start'])
In [1457]:
data.dtypes
Out[1457]:
utc_date                 datetime64[ns]
account_key                       int64
registration_date        datetime64[ns]
subscription_start       datetime64[ns]
course_key                       object
sibling_key                      object
course_title                     object
has_visited                     float64
total_minutes_visited           float64
lessons_completed               float64
projects_completed              float64
dtype: object
In [1458]:
data.dtypes
Out[1458]:
utc_date                 datetime64[ns]
account_key                       int64
registration_date        datetime64[ns]
subscription_start       datetime64[ns]
course_key                       object
sibling_key                      object
course_title                     object
has_visited                     float64
total_minutes_visited           float64
lessons_completed               float64
projects_completed              float64
dtype: object
In [1459]:
data
Out[1459]:
utc_date account_key registration_date subscription_start course_key sibling_key course_title has_visited total_minutes_visited lessons_completed projects_completed
0 2014-11-05 448 2014-08-05 2014-11-05 ud359-nd ud359 Intro to Data Science 0.0 0.0 0.0 0.0
1 2014-11-05 448 2014-08-05 2014-11-05 ud120-nd ud120 Intro to Machine Learning 0.0 0.0 0.0 0.0
2 2014-11-05 448 2014-08-05 2014-11-05 ud651-nd ud651 Data Analysis with R 0.0 0.0 0.0 0.0
3 2014-11-05 448 2014-08-05 2014-11-05 ud507-nd ud507 Data Visualization and D3.js 0.0 0.0 0.0 0.0
4 2014-11-05 448 2014-08-05 2014-11-05 ud651 ud651 Data Analysis with R 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ...
2309234 2015-08-26 854 2012-10-12 2015-08-23 ud359 ud359 Intro to Data Science 0.0 0.0 0.0 0.0
2309235 2015-08-26 854 2012-10-12 2015-08-23 ud804 ud804 JavaScript Basics 0.0 0.0 0.0 0.0
2309236 2015-08-26 854 2012-10-12 2015-08-23 ud651 ud651 Data Analysis with R 0.0 0.0 0.0 0.0
2309237 2015-08-26 854 2012-10-12 2015-08-23 ud304 ud304 Intro to HTML and CSS 0.0 0.0 0.0 0.0
2309238 2015-08-26 854 2012-10-12 2015-08-23 nd002 nd002 Data Analyst Nanodegree 0.0 0.0 0.0 0.0

2275075 rows × 11 columns

In [1460]:
print(data['course_title'].unique())
print(len(data['course_title'].unique()))
['Intro to Data Science' 'Intro to Machine Learning'
 'Data Analysis with R' 'Data Visualization and D3.js' 'JavaScript Basics'
 'Data Wrangling with MongoDB' 'Intro to HTML and CSS'
 'Data Analyst Nanodegree' 'A/B Testing' 'ud134-nd' 'ud134a-nd']
11
In [1461]:
print(data['sibling_key'].unique())
print(len(data['sibling_key'].unique()))
['ud359' 'ud120' 'ud651' 'ud507' 'ud804' 'ud032' 'ud304' 'nd002' 'ud257'
 'ud134' 'ud134a']
11
In [1462]:
print(data['course_key'].unique())
print(len(data['course_key'].unique()))
['ud359-nd' 'ud120-nd' 'ud651-nd' 'ud507-nd' 'ud651' 'ud804-nd' 'ud032-nd'
 'ud304-nd' 'ud120' 'ud359' 'ud032' 'ud507' 'ud304' 'ud804' 'nd002'
 'ud257-nd' 'ud257' 'ud134-nd' 'ud134a-nd']
19
It seems that the same values are common between the 'sibling_key' and 'course_key'¶
In [1463]:
plotData_subplot(data, 'course_title vs sibling_key','course_title', 'sibling_key')

it seems that both columns 'course_title' and 'sibling_key' represent the same data so let's check that using represent_same¶

In [1464]:
represent_same(data, 'course_title', 'sibling_key')
Out[1464]:
True
In [1465]:
print(data['course_key'].unique())
print(len(data['course_key'].unique()))
['ud359-nd' 'ud120-nd' 'ud651-nd' 'ud507-nd' 'ud651' 'ud804-nd' 'ud032-nd'
 'ud304-nd' 'ud120' 'ud359' 'ud032' 'ud507' 'ud304' 'ud804' 'nd002'
 'ud257-nd' 'ud257' 'ud134-nd' 'ud134a-nd']
19
In [1466]:
user_course = data.groupby(['account_key', 'course_title'])
In [1467]:
user_course.first()
Out[1467]:
utc_date registration_date subscription_start course_key sibling_key has_visited total_minutes_visited lessons_completed projects_completed
account_key course_title
0 A/B Testing 2015-03-05 2015-01-09 2015-01-09 ud257-nd ud257 0.0 0.000000 0.0 0.0
Data Analysis with R 2015-01-09 2015-01-09 2015-01-09 ud651-nd ud651 0.0 0.000000 0.0 0.0
Data Analyst Nanodegree 2015-01-09 2015-01-09 2015-01-09 nd002 nd002 1.0 11.679374 0.0 0.0
Data Visualization and D3.js 2015-01-09 2015-01-09 2015-01-09 ud507-nd ud507 0.0 0.000000 0.0 0.0
Data Wrangling with MongoDB 2015-01-09 2015-01-09 2015-01-09 ud032 ud032 0.0 0.000000 0.0 0.0
... ... ... ... ... ... ... ... ... ... ...
1305 Data Wrangling with MongoDB 2015-05-12 2014-10-31 2015-05-12 ud032-nd ud032 0.0 0.000000 0.0 0.0
Intro to Data Science 2015-05-12 2014-10-31 2015-05-12 ud359-nd ud359 0.0 0.000000 0.0 0.0
Intro to HTML and CSS 2015-05-12 2014-10-31 2015-05-12 ud304-nd ud304 0.0 0.000000 0.0 0.0
Intro to Machine Learning 2015-05-12 2014-10-31 2015-05-12 ud120-nd ud120 0.0 0.000000 0.0 0.0
JavaScript Basics 2015-05-12 2014-10-31 2015-05-12 ud804-nd ud804 0.0 0.000000 0.0 0.0

12382 rows × 9 columns

In [1468]:
course_title_key = data.groupby(['course_title', 'course_key'])
In [1469]:
course_title_key.first()
Out[1469]:
utc_date account_key registration_date subscription_start sibling_key has_visited total_minutes_visited lessons_completed projects_completed
course_title course_key
A/B Testing ud257 2015-03-05 448 2014-08-05 2015-01-27 ud257 0.0 0.0 0.0 0.0
ud257-nd 2015-03-05 448 2014-08-05 2015-01-27 ud257 0.0 0.0 0.0 0.0
Data Analysis with R ud651 2014-11-05 448 2014-08-05 2014-11-05 ud651 0.0 0.0 0.0 0.0
ud651-nd 2014-11-05 448 2014-08-05 2014-11-05 ud651 0.0 0.0 0.0 0.0
Data Analyst Nanodegree nd002 2014-11-05 448 2014-08-05 2014-11-05 nd002 0.0 0.0 0.0 0.0
Data Visualization and D3.js ud507 2014-11-05 448 2014-08-05 2014-11-05 ud507 0.0 0.0 0.0 0.0
ud507-nd 2014-11-05 448 2014-08-05 2014-11-05 ud507 0.0 0.0 0.0 0.0
Data Wrangling with MongoDB ud032 2014-11-05 448 2014-08-05 2014-11-05 ud032 0.0 0.0 0.0 0.0
ud032-nd 2014-11-05 448 2014-08-05 2014-11-05 ud032 0.0 0.0 0.0 0.0
Intro to Data Science ud359 2014-11-05 448 2014-08-05 2014-11-05 ud359 0.0 0.0 0.0 0.0
ud359-nd 2014-11-05 448 2014-08-05 2014-11-05 ud359 0.0 0.0 0.0 0.0
Intro to HTML and CSS ud304 2014-11-05 448 2014-08-05 2014-11-05 ud304 0.0 0.0 0.0 0.0
ud304-nd 2014-11-05 448 2014-08-05 2014-11-05 ud304 0.0 0.0 0.0 0.0
Intro to Machine Learning ud120 2014-11-05 448 2014-08-05 2014-11-05 ud120 0.0 0.0 0.0 0.0
ud120-nd 2014-11-05 448 2014-08-05 2014-11-05 ud120 0.0 0.0 0.0 0.0
JavaScript Basics ud804 2014-11-05 448 2014-08-05 2014-11-05 ud804 0.0 0.0 0.0 0.0
ud804-nd 2014-11-05 448 2014-08-05 2014-11-05 ud804 0.0 0.0 0.0 0.0
ud134-nd ud134-nd 2015-07-04 448 2014-08-05 2015-03-10 ud134 0.0 0.0 0.0 0.0
ud134a-nd ud134a-nd 2015-07-30 448 2014-08-05 2015-03-10 ud134a 0.0 0.0 0.0 0.0

it seems that there's more than one 'course_key' for each 'course_title', so let's ensure this with a function¶

In [1470]:
def course_key_dict(df, col1, col2):
    col1_col2 = {}   
    for course in df[col1].unique():
        values = []
        for ck in df[col2][df[col1]==course]:
            values.append(ck)
        col1_col2[course] = set(values)
    
    print(col1_col2)
    print(len(col1_col2))
    return col1_col2
In [1471]:
course_title_key = course_key_dict(data, 'course_title', 'course_key')
{'Intro to Data Science': {'ud359-nd', 'ud359'}, 'Intro to Machine Learning': {'ud120', 'ud120-nd'}, 'Data Analysis with R': {'ud651', 'ud651-nd'}, 'Data Visualization and D3.js': {'ud507', 'ud507-nd'}, 'JavaScript Basics': {'ud804', 'ud804-nd'}, 'Data Wrangling with MongoDB': {'ud032-nd', 'ud032'}, 'Intro to HTML and CSS': {'ud304-nd', 'ud304'}, 'Data Analyst Nanodegree': {'nd002'}, 'A/B Testing': {'ud257-nd', 'ud257'}, 'ud134-nd': {'ud134-nd'}, 'ud134a-nd': {'ud134a-nd'}}
11
In [1472]:
course_title_sibling = course_key_dict(data, 'course_title', 'sibling_key')
{'Intro to Data Science': {'ud359'}, 'Intro to Machine Learning': {'ud120'}, 'Data Analysis with R': {'ud651'}, 'Data Visualization and D3.js': {'ud507'}, 'JavaScript Basics': {'ud804'}, 'Data Wrangling with MongoDB': {'ud032'}, 'Intro to HTML and CSS': {'ud304'}, 'Data Analyst Nanodegree': {'nd002'}, 'A/B Testing': {'ud257'}, 'ud134-nd': {'ud134'}, 'ud134a-nd': {'ud134a'}}
11
So we can dispense with both: 'course_key' and 'sibling_key', as both just refer to the course title and gives no more needed details¶
In [1473]:
data.drop('sibling_key', inplace=True, axis=1)
data.drop('course_key', inplace=True, axis=1)
In [1474]:
data
Out[1474]:
utc_date account_key registration_date subscription_start course_title has_visited total_minutes_visited lessons_completed projects_completed
0 2014-11-05 448 2014-08-05 2014-11-05 Intro to Data Science 0.0 0.0 0.0 0.0
1 2014-11-05 448 2014-08-05 2014-11-05 Intro to Machine Learning 0.0 0.0 0.0 0.0
2 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0 0.0
3 2014-11-05 448 2014-08-05 2014-11-05 Data Visualization and D3.js 0.0 0.0 0.0 0.0
4 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ...
2309234 2015-08-26 854 2012-10-12 2015-08-23 Intro to Data Science 0.0 0.0 0.0 0.0
2309235 2015-08-26 854 2012-10-12 2015-08-23 JavaScript Basics 0.0 0.0 0.0 0.0
2309236 2015-08-26 854 2012-10-12 2015-08-23 Data Analysis with R 0.0 0.0 0.0 0.0
2309237 2015-08-26 854 2012-10-12 2015-08-23 Intro to HTML and CSS 0.0 0.0 0.0 0.0
2309238 2015-08-26 854 2012-10-12 2015-08-23 Data Analyst Nanodegree 0.0 0.0 0.0 0.0

2275075 rows × 9 columns

In [1475]:
#plotData_subplot(data, 'course_title vs course_key', 'course_title', 'course_key')

it seems that both columns 'course_title' and 'course_key' represent the same data so let's check that¶

In [1476]:
#course_key_dict(data, 'account_key', 'course_title')
In [1477]:
def stats(df, col1, col2, threshold, title):
    counter = {}   
    total = 0
    for c_key in df[col1].unique():
        cnt=0
        for v in df[col2][df[col1]==c_key]:
            if v>=threshold:
                cnt+=1
        total+=cnt
        counter[c_key] = cnt
    
    #print(counter)
    print(len(counter))
    print(f"{title}: {total}")
    return counter
In [1478]:
visited_dict = stats(data, 'course_title', 'has_visited', 1, "Total number of visits for all courses")
11
Total number of visits for all courses: 45865
In [1486]:
visited_dict
Out[1486]:
{'Intro to Data Science': 13958,
 'Intro to Machine Learning': 4699,
 'Data Analysis with R': 5355,
 'Data Visualization and D3.js': 1584,
 'JavaScript Basics': 232,
 'Data Wrangling with MongoDB': 11073,
 'Intro to HTML and CSS': 348,
 'Data Analyst Nanodegree': 7507,
 'A/B Testing': 354,
 'ud134-nd': 716,
 'ud134a-nd': 39}
In [1487]:
minutes_dict = stats(data, 'course_title', 'total_minutes_visited', 1, "Total number of visits for all courses")
11
Total number of visits for all courses: 45865
In [1488]:
minutes_dict
Out[1488]:
{'Intro to Data Science': 13958,
 'Intro to Machine Learning': 4699,
 'Data Analysis with R': 5355,
 'Data Visualization and D3.js': 1584,
 'JavaScript Basics': 232,
 'Data Wrangling with MongoDB': 11073,
 'Intro to HTML and CSS': 348,
 'Data Analyst Nanodegree': 7507,
 'A/B Testing': 354,
 'ud134-nd': 716,
 'ud134a-nd': 39}
In [1489]:
visited_dict == minutes_dict
Out[1489]:
True

Actually 'total_minutes_visited' column represents also the 'has_visited' column, so we can remove the 'has_visited' column¶

In [1491]:
data.drop('has_visited', inplace=True, axis=1)
In [1492]:
data
Out[1492]:
utc_date account_key registration_date subscription_start course_title total_minutes_visited lessons_completed projects_completed
0 2014-11-05 448 2014-08-05 2014-11-05 Intro to Data Science 0.0 0.0 0.0
1 2014-11-05 448 2014-08-05 2014-11-05 Intro to Machine Learning 0.0 0.0 0.0
2 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0
3 2014-11-05 448 2014-08-05 2014-11-05 Data Visualization and D3.js 0.0 0.0 0.0
4 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0
... ... ... ... ... ... ... ... ...
2309234 2015-08-26 854 2012-10-12 2015-08-23 Intro to Data Science 0.0 0.0 0.0
2309235 2015-08-26 854 2012-10-12 2015-08-23 JavaScript Basics 0.0 0.0 0.0
2309236 2015-08-26 854 2012-10-12 2015-08-23 Data Analysis with R 0.0 0.0 0.0
2309237 2015-08-26 854 2012-10-12 2015-08-23 Intro to HTML and CSS 0.0 0.0 0.0
2309238 2015-08-26 854 2012-10-12 2015-08-23 Data Analyst Nanodegree 0.0 0.0 0.0

2275075 rows × 8 columns

In [1498]:
def stats_study(df, col1, col2, title):
    counter = {}   
    total = 0
    for c_key in df[col1].unique():
        cnt=0
        for v in df[col2][df[col1]==c_key]:
            cnt+=v/60
        total+=cnt
        counter[c_key] = cnt
    
    print(len(counter))
    print(f"{title}: {total} hour")
    return counter
In [1499]:
total_study_minutes_per_user = stats_study(data, 'account_key', 'total_minutes_visited', "Total study hour per each student")
1237
Total study hour per each student: 55024.38935129667 hour
In [1500]:
total_study_minutes_per_user
Out[1500]:
{448: 21.173192013890336,
 44: 71.18038214720835,
 258: 99.50050039167105,
 366: 105.81184049721902,
 587: 62.6561287222055,
 943: 0.26470904722166666,
 412: 123.48049746669066,
 837: 0.0,
 1288: 2.869097194443334,
 1006: 1.8403854222233333,
 429: 48.88846973608816,
 130: 96.78687172223566,
 31: 57.22401807498382,
 919: 100.70693086666853,
 204: 181.77559803617393,
 439: 170.21245273887598,
 72: 65.00673827497684,
 580: 110.54249786947767,
 225: 105.71674153332118,
 179: 90.19885272778366,
 224: 118.02267986666554,
 553: 159.76570674169494,
 1094: 10.678149669457332,
 649: 66.80892004999664,
 655: 21.992219833343334,
 336: 163.6031518861051,
 150: 135.11573245555178,
 408: 33.88229169167434,
 458: 28.562698436086496,
 494: 104.13453096943348,
 369: 264.70542058060903,
 913: 3.332299638888333,
 601: 17.362784130564997,
 506: 165.52130688055496,
 156: 70.48307957779967,
 963: 0.6913395083333334,
 522: 74.48242225275705,
 595: 106.83973580555318,
 480: 43.645340455553175,
 544: 99.20322302218803,
 378: 21.598214663884836,
 810: 0.9555094138883333,
 81: 88.16668886389235,
 483: 171.07328314718654,
 1174: 69.93111993889734,
 512: 35.13442967499266,
 604: 100.59365270830246,
 568: 135.3248918610859,
 700: 2.038815158338333,
 91: 79.82915643331202,
 268: 33.185371913887835,
 550: 87.23815363612675,
 1300: 0.22312028611116666,
 90: 76.9650940749725,
 584: 60.696732477770496,
 431: 118.07736443052218,
 51: 38.00312405277166,
 1214: 0.21370871666666666,
 309: 66.2634324944628,
 537: 52.517507322237826,
 206: 117.69651682499284,
 18: 32.780236422232676,
 582: 97.83540634720832,
 882: 0.6043669249999999,
 954: 2.2386210305566663,
 368: 147.9785226944667,
 864: 0.8854931416666666,
 576: 64.74443081944484,
 10: 113.10871384445636,
 329: 126.01125465556765,
 1142: 2.143179208332667,
 195: 159.6637193472855,
 140: 416.52285847221674,
 223: 70.82039259723815,
 1204: 12.068653330553834,
 198: 313.7220930833003,
 1071: 0.727295944445,
 322: 31.961114030556338,
 323: 120.19136000557965,
 752: 0.5099196027778333,
 504: 78.76682504723163,
 696: 19.675778441666665,
 114: 23.643517441663334,
 520: 41.614300577760154,
 1220: 2.320878883333333,
 838: 0.045449058333333334,
 3: 73.70031531391932,
 230: 94.26611806390719,
 1058: 43.87712364168184,
 821: 1.773315844445,
 420: 54.41550172782065,
 890: 0.25240198611166664,
 1098: 157.2283586694636,
 312: 74.17286430834834,
 428: 302.2401253888618,
 301: 82.88593287222113,
 755: 5.389995452776834,
 157: 82.7535167777945,
 315: 120.61288985830767,
 194: 133.4056516527712,
 1122: 0.8776045277771667,
 12: 172.91603486665244,
 23: 173.57741814442312,
 562: 118.22152043052564,
 339: 87.73354474167667,
 1047: 0.08807521111116667,
 123: 23.763322941661002,
 60: 35.092682774997165,
 328: 147.63166161385166,
 390: 110.08442778889781,
 45: 149.50840100838204,
 1020: 0.8031925194438333,
 145: 127.81616659166562,
 626: 3.2824261944455,
 1243: 1.0485239388883334,
 1253: 2.703917191668333,
 354: 128.3166950416888,
 525: 87.36434034725117,
 620: 82.80258378609417,
 760: 0.8500002638883333,
 612: 57.22890802221399,
 892: 1.0176705472216667,
 394: 234.7084594333138,
 165: 74.27357577222666,
 102: 123.25543395001897,
 1244: 4.191212897220501,
 443: 105.53371141111944,
 1121: 34.78779709999217,
 740: 0.12985982777783334,
 106: 63.092548338902816,
 1107: 1.2271494305566666,
 234: 181.46381543337796,
 630: 58.873173822226654,
 1090: 0.4304960333333333,
 148: 48.61583668056016,
 57: 128.07242427500017,
 1040: 6.061170622220333,
 1019: 32.84934734443316,
 1117: 0.0,
 675: 0.5027436166666667,
 85: 142.07608483332913,
 1083: 0.39199742777783336,
 1011: 8.949917294444667,
 1139: 53.783446669464496,
 440: 69.96898781113048,
 745: 0.0,
 1111: 26.87443513055117,
 25: 80.66723815829616,
 367: 57.503335761117164,
 160: 53.99199323889783,
 1118: 3.1811707333345,
 1108: 0.7494795888883333,
 92: 117.06683595555434,
 335: 87.554504180552,
 275: 122.02474658332635,
 800: 34.59271806667502,
 617: 219.42989276386587,
 161: 82.008321777775,
 101: 111.54780479442654,
 187: 111.45582338614084,
 929: 0.0,
 1021: 0.050964050000000004,
 541: 108.5318540277347,
 495: 73.34717658886767,
 500: 85.69437379167515,
 756: 0.0,
 577: 86.34290047221133,
 591: 39.95246345275849,
 1114: 0.0,
 185: 95.43100180830498,
 599: 35.46818478888001,
 227: 36.816998788872844,
 441: 104.68671326946213,
 640: 55.374228241651664,
 181: 90.91196344443901,
 930: 0.0,
 283: 126.29895426391425,
 702: 1.9414435472233331,
 712: 46.32040999446066,
 639: 51.38145707501235,
 839: 0.22645091666666667,
 1161: 1.4635392861116667,
 969: 0.41611503888833334,
 231: 52.38686506389098,
 672: 11.572675244456667,
 273: 145.70310299996993,
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 1115: 0.0,
 555: 28.040877947228335,
 863: 0.0,
 1104: 0.6238944805555,
 177: 29.124622988906665,
 104: 203.13637032218813,
 920: 12.415562752778333,
 676: 0.33058724722283334,
 735: 4.057214644437833,
 146: 29.925660641642832,
 609: 20.13066353611,
 299: 20.81988151388833,
 240: 38.32167511666233,
 267: 6.242718263890999,
 107: 19.124981722221666,
 401: 33.505946680544994,
 201: 0.0,
 167: 10.595873258336667,
 262: 9.541365638887667,
 797: 0.0,
 212: 8.435533097216,
 94: 42.834146105557316,
 574: 11.338532205551166,
 353: 23.940795066656168,
 302: 15.914418091671,
 939: 0.0,
 370: 3.9790809638828333,
 551: 31.414545922203338,
 447: 8.202963863883832,
 49: 14.2118981722105,
 396: 7.893719702779,
 11: 10.01815312499,
 536: 11.597148747223997,
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 387: 8.7499715638895,
 376: 76.67313312221272,
 360: 73.85017533610483,
 811: 26.707314077773333,
 1: 65.71164312222884,
 937: 13.009122288883834,
 84: 66.41184158886284,
 948: 1.944132897221667,
 116: 75.59087931944433,
 109: 57.71325905557433,
 1024: 4.060320938895,
 1004: 1.933853775,
 8: 74.10116333333299,
 894: 2.5396386638895,
 255: 114.00093706943531,
 704: 1.1279341166666665,
 1113: 0.6201614027783333,
 407: 27.399797444447827,
 1163: 1.3985758305545,
 466: 59.724003708328006,
 274: 167.15564110557833,
 927: 24.57849778612283,
 382: 22.68876208889,
 211: 90.2296590166365,
 647: 43.118090536087834,
 1202: 7.5197878749993325,
 320: 40.87881267777617,
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 484: 149.229119536165,
 635: 5.176653452783333,
 173: 55.17575367779116,
 559: 83.53534424443944,
 1022: 0.9933087388899999,
 471: 104.88154527779251,
 228: 67.73904762500499,
 89: 60.09886668054484,
 886: 0.8862592777776667,
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 970: 12.294902947222168,
 422: 37.21611426942166,
 385: 78.74940774723818,
 1173: 48.07254461111333,
 476: 21.19779453333883,
 182: 51.883570766683675,
 298: 50.27827768054949,
 718: 8.514615261111665,
 1055: 2.2659917055566665,
 540: 57.006323252793834,
 475: 78.54336559166381,
 58: 133.23638775835227,
 175: 126.71247332224402,
 825: 2.3334870361111664,
 154: 55.152629477775676,
 1275: 0.0,
 192: 23.1124053083315,
 479: 29.344741827786166,
 1062: 6.777278636117666,
 341: 39.19094767221333,
 330: 56.571730216685516,
 293: 49.77288342779549,
 236: 29.034730380579003,
 304: 63.7726792055645,
 487: 27.150565544446664,
 1282: 22.25709063610167,
 608: 37.27303831109765,
 406: 15.531378069437666,
 276: 36.6493013138955,
 942: 0.5163088,
 779: 0.07100674166666666,
 237: 55.19909373334168,
 20: 37.865691336115994,
 511: 34.60061393331284,
 1064: 0.3639213138883333,
 398: 96.70487019446115,
 314: 125.93823334441744,
 955: 4.778315233337834,
 27: 83.66455536668099,
 995: 0.38041358333333336,
 1249: 12.653304613888501,
 714: 2.5338293083338335,
 331: 55.19040848888434,
 287: 60.39584677222449,
 1026: 0.21715296666666667,
 1030: 6.00108761111,
 162: 94.09735046109715,
 303: 205.0208993555207,
 533: 38.48142643889051,
 1183: 47.171329824995,
 881: 2.7925020222221666,
 260: 37.92834751109551,
 519: 42.775582822187324,
 1182: 20.029978727776665,
 928: 8.813423222221667,
 1215: 41.07777746390883,
 501: 34.021936480566666,
 1099: 7.555687330556165,
 1250: 6.269196216666667,
 648: 54.64167700277617,
 1151: 9.718451380543833,
 17: 41.36547038889633,
 120: 20.58120075833217,
 129: 51.99292408054934,
 242: 63.83146469166563,
 235: 52.683709963877824,
 4: 62.04414750277599,
 1029: 0.3300630861116667,
 497: 52.71514036110884,
 665: 0.9123401333321668,
 858: 34.033965777779336,
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 1305: 0.3760972916659999,
 200: 62.31064093609264,
 566: 77.23068936665152,
 852: 76.49303782779867,
 713: 2.3176754833388333,
 556: 62.565769544455826,
 1091: 0.15630452777783335,
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 758: 0.0371652055555,
 252: 37.54493029720383,
 621: 39.62169096665799,
 189: 69.58006593333151,
 815: 1.0842561666666666,
 491: 102.23767524723515,
 184: 32.64782281665567,
 669: 2.0252532388883333,
 1007: 0.9996648277783333,
 410: 73.81917760556499,
 61: 37.05831016944285,
 7: 36.61562376945951,
 1052: 7.238776438883834,
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 661: 17.282298177771665,
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 846: 4.704209502777167,
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 596: 52.43604864445382,
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 36: 38.456839566677345,
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 41: 48.487892838881,
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 988: 1.1828668277778331,
 877: 0.44205163611166665,
 16: 24.115656905571004,
 361: 50.13320212225018,
 644: 75.10396529723215,
 616: 34.544155508330995,
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 1068: 0.4620878388883333,
 513: 46.118172119423335,
 481: 59.27396218613051,
 63: 49.90644459724516,
 ...}
In [1511]:
def plotDict(dictionary, num, xl, yl, title):
    sorted_dict = dict(sorted(dictionary.items(), key=lambda x:x[1]))
    df = pd.DataFrame.from_dict(sorted_dict, orient='index')
    pd_toPlot = pd.concat([df.head(num), df.tail(num)])
    
    #return pd_toPlot
    #pd_toPlot.plot(kind='bar', title=title, figsize=(12, 6))
    #c = ['red', 'yellow', 'black', 'blue', 'orange']
    pd_toPlot.plot(kind='bar', title=title, figsize=(12, 6))

    plt.xlabel(xl)
    plt.ylabel(yl)
    plt.savefig(f'{title}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
    plt.show()
    print(pd_toPlot)
In [1523]:
plotDict(total_study_minutes_per_user, 5, 'account_key', "hours", "user_studying_hours")
               0
837     0.000000
1117    0.000000
745     0.000000
929     0.000000
756     0.000000
428   302.240125
277   302.869608
198   313.722093
546   329.395111
140   416.522858
In [1524]:
total_study_minutes_per_course = stats_study(data, 'course_title', 'total_minutes_visited', "Total study hour per each student")
9
Total study hour per each student: 54047.517509152225 hour
In [1525]:
total_study_minutes_per_course
Out[1525]:
{'Intro to Data Science': 20605.002014807724,
 'Intro to Machine Learning': 6091.752816405661,
 'Data Analysis with R': 6953.553592002642,
 'Data Visualization and D3.js': 1796.9486961333712,
 'JavaScript Basics': 258.1663076249884,
 'Data Wrangling with MongoDB': 14864.731985163948,
 'Intro to HTML and CSS': 343.51694648333955,
 'Data Analyst Nanodegree': 2663.789592283335,
 'A/B Testing': 470.05555824721944}
In [1526]:
plotDict(total_study_minutes_per_course, 5, 'course_studied_hours', "hours", "course studying hours")
                                         0
JavaScript Basics               258.166308
Intro to HTML and CSS           343.516946
A/B Testing                     470.055558
Data Visualization and D3.js   1796.948696
Data Analyst Nanodegree        2663.789592
Data Analyst Nanodegree        2663.789592
Intro to Machine Learning      6091.752816
Data Analysis with R           6953.553592
Data Wrangling with MongoDB   14864.731985
Intro to Data Science         20605.002015
Out of abut 55024 hours of studing for all courses there's a 976.86 hours are spent for: 'ud134a-nd' (21.46) and 'ud134-nd' (955.4), their percentage from the total number of hours = 1.77% ,actually it seems that there's an error while inserting courses values to the column as those values are tipical to that of 'sibling_key' patterns and that an output for each 'course_title' corresponding to its 'course_key' before removing 'course_key' column: 'A/B Testing': {'ud257-nd', 'ud257'}, 'ud134-nd': {'ud134-nd'}, 'ud134a-nd': {'ud134a-nd'}, for me it seems that the both values 'ud134a-nd' and 'ud134-nd' represents the same course as the output shows above, so as it don't know what's the course or the category of knowledge it represents and also it has a small percentage in the studied hours so I'm going to drop them out from the dataframe¶
In [1527]:
unknown_courses_df
Out[1527]:
utc_date account_key registration_date subscription_start course_title total_minutes_visited lessons_completed projects_completed
3811 2015-07-04 448 2014-08-05 2015-03-10 ud134-nd 0.000000 0.0 0.0
3822 2015-07-05 448 2014-08-05 2015-03-10 ud134-nd 0.000000 0.0 0.0
3845 2015-07-06 448 2014-08-05 2015-03-10 ud134-nd 0.000000 0.0 0.0
3864 2015-07-07 448 2014-08-05 2015-03-10 ud134-nd 0.000000 0.0 0.0
3881 2015-07-08 448 2014-08-05 2015-03-10 ud134-nd 0.000000 0.0 0.0
... ... ... ... ... ... ... ... ...
2309192 2015-08-24 854 2012-10-12 2015-08-23 ud134a-nd 0.000000 0.0 0.0
2309206 2015-08-25 854 2012-10-12 2015-08-23 ud134-nd 0.000000 0.0 0.0
2309207 2015-08-25 854 2012-10-12 2015-08-23 ud134a-nd 0.000000 0.0 0.0
2309226 2015-08-26 854 2012-10-12 2015-08-23 ud134-nd 0.000000 0.0 0.0
2309233 2015-08-26 854 2012-10-12 2015-08-23 ud134a-nd 42.222797 1.0 0.0

52965 rows × 8 columns

unknown_courses_df (52965) percentage represents 2.32% from the whole data (2275075)¶
In [1528]:
unknown_courses = ['ud134a-nd', 'ud134-nd']
unknown_courses_df = data.loc[data['course_title'].isin(unknown_courses)]
In [1529]:
data.drop(unknown_courses_df.index, inplace=True)
In [1530]:
data
Out[1530]:
utc_date account_key registration_date subscription_start course_title total_minutes_visited lessons_completed projects_completed
0 2014-11-05 448 2014-08-05 2014-11-05 Intro to Data Science 0.0 0.0 0.0
1 2014-11-05 448 2014-08-05 2014-11-05 Intro to Machine Learning 0.0 0.0 0.0
2 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0
3 2014-11-05 448 2014-08-05 2014-11-05 Data Visualization and D3.js 0.0 0.0 0.0
4 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0
... ... ... ... ... ... ... ... ...
2309234 2015-08-26 854 2012-10-12 2015-08-23 Intro to Data Science 0.0 0.0 0.0
2309235 2015-08-26 854 2012-10-12 2015-08-23 JavaScript Basics 0.0 0.0 0.0
2309236 2015-08-26 854 2012-10-12 2015-08-23 Data Analysis with R 0.0 0.0 0.0
2309237 2015-08-26 854 2012-10-12 2015-08-23 Intro to HTML and CSS 0.0 0.0 0.0
2309238 2015-08-26 854 2012-10-12 2015-08-23 Data Analyst Nanodegree 0.0 0.0 0.0

2222110 rows × 8 columns

In [1544]:
# calculates the sum per any column that the user wants to sum
def stats_study_1(df, col1, col2, title):
    counter = {}   
    total = 0
    for c_key in df[col1].unique():
        cnt=0
        for v in df[col2][df[col1]==c_key]:
            cnt+=v
        total+=cnt
        counter[c_key] = cnt
    
    print(len(counter))
    print(f"{title}: {total}")
    return counter
In [1545]:
total_study_lessons_per_course = stats_study_1(data, 'course_title', 'lessons_completed', "Total study hour per each student")
9
Total study hour per each student: 16558.0
In [1546]:
plotDict(total_study_lessons_per_course, 5, 'course_studied_lessons', "lessons", "studied lessons per each course")
                                   0
A/B Testing                    133.0
Intro to HTML and CSS          143.0
JavaScript Basics              233.0
Data Analyst Nanodegree        604.0
Data Visualization and D3.js   645.0
Data Visualization and D3.js   645.0
Data Analysis with R          2474.0
Intro to Machine Learning     2499.0
Data Wrangling with MongoDB   4817.0
Intro to Data Science         5010.0
In [1547]:
total_done_projects_per_course = stats_study_1(data, 'course_title', 'projects_completed', "Total done projects per each student")
9
Total done projects per each student: 917.0
In [1548]:
plotDict(total_done_projects_per_course, 5, "user account key", "projects", 'done_projects')
                                  0
A/B Testing                     0.0
JavaScript Basics               2.0
Intro to HTML and CSS           2.0
Data Visualization and D3.js   36.0
Data Analyst Nanodegree        43.0
Data Analyst Nanodegree        43.0
Intro to Machine Learning      87.0
Data Analysis with R          152.0
Data Wrangling with MongoDB   253.0
Intro to Data Science         342.0

In terms of the top most important topics we can take the most common four nanodegrees as follows:¶

  • Intro to Data Science
  • Data Wrangling with MongoDB
  • Data Analysis with R / Intro to Machine Learning #### so udacity can make more offers for those topics of study to meet user interests, also can create a new nanodegree which may contain about three projects each one covers one of those topics which may be preferred by alot of users
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [1549]:
total_study_lessons_per_user = stats_study_1(data, 'account_key', 'lessons_completed', "Total study hour per each student")
1237
Total study hour per each student: 16558.0
In [1550]:
plotDict(total_study_lessons_per_user, 5, "user account key", "lessons", 'course_studied_lessons')
         0
448    0.0
943    0.0
837    0.0
1288   0.0
1006   0.0
379   62.0
171   64.0
336   65.0
597   65.0
139   66.0
In [1551]:
total_study_projects_per_user = stats_study_1(data, 'account_key', 'projects_completed', "Total study hour per each student")
1237
Total study hour per each student: 917.0
In [1552]:
plotDict(total_study_projects_per_user, 5, "user account key", "lessons", 'projects_done')
        0
587   0.0
943   0.0
837   0.0
1288  0.0
1006  0.0
532   6.0
99    6.0
132   6.0
139   6.0
474   8.0
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [1553]:
print(data['course_title'].unique())
print(len(data['course_title'].unique()))
['Intro to Data Science' 'Intro to Machine Learning'
 'Data Analysis with R' 'Data Visualization and D3.js' 'JavaScript Basics'
 'Data Wrangling with MongoDB' 'Intro to HTML and CSS'
 'Data Analyst Nanodegree' 'A/B Testing']
9
In [1554]:
help(stats)
Help on function stats in module __main__:

stats(df, col1, col2, threshold, title)

In [1555]:
stats(data, 'course_title', 'lessons_completed', 1, "course per number of completed lessons")
9
course per number of completed lessons: 12870
Out[1555]:
{'Intro to Data Science': 3988,
 'Intro to Machine Learning': 1759,
 'Data Analysis with R': 2002,
 'Data Visualization and D3.js': 468,
 'JavaScript Basics': 109,
 'Data Wrangling with MongoDB': 3761,
 'Intro to HTML and CSS': 108,
 'Data Analyst Nanodegree': 595,
 'A/B Testing': 80}
In [1556]:
stats(data, 'course_title', 'projects_completed', 1, "course per number of completed projects")
9
course per number of completed projects: 917
Out[1556]:
{'Intro to Data Science': 342,
 'Intro to Machine Learning': 87,
 'Data Analysis with R': 152,
 'Data Visualization and D3.js': 36,
 'JavaScript Basics': 2,
 'Data Wrangling with MongoDB': 253,
 'Intro to HTML and CSS': 2,
 'Data Analyst Nanodegree': 43,
 'A/B Testing': 0}
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 

Now let's see what's the most year that the user's achieved in their courses¶

In [1559]:
pd.DatetimeIndex(data['subscription_start']).year.unique()
Out[1559]:
Int64Index([2014, 2015], dtype='int64', name='subscription_start')
In [1562]:
pd.DatetimeIndex(data['utc_date']).year.unique()
Out[1562]:
Int64Index([2014, 2015], dtype='int64', name='utc_date')
In [ ]:
 
In [1564]:
pd.DatetimeIndex(data['registration_date']).year.unique()
Out[1564]:
Int64Index([2014, 2013, 2012, 2015, 1816], dtype='int64', name='registration_date')
In [1568]:
data['registration_date'][pd.DatetimeIndex(data['registration_date']).year==1816].count()
Out[1568]:
34
In [1569]:
year_1816 = data[pd.DatetimeIndex(data['registration_date']).year==1816]
In [1570]:
year_1816
Out[1570]:
utc_date account_key registration_date subscription_start course_title total_minutes_visited lessons_completed projects_completed
2308289 2015-08-23 880 1816-03-30 2015-08-23 Intro to Machine Learning 0.000000 0.0 0.0
2308290 2015-08-23 880 1816-03-30 2015-08-23 Data Visualization and D3.js 0.000000 0.0 0.0
2308291 2015-08-23 880 1816-03-30 2015-08-23 Data Wrangling with MongoDB 0.000000 0.0 0.0
2308292 2015-08-23 880 1816-03-30 2015-08-23 Intro to HTML and CSS 0.000000 0.0 0.0
2308293 2015-08-23 880 1816-03-30 2015-08-23 JavaScript Basics 0.000000 0.0 0.0
2308294 2015-08-23 880 1816-03-30 2015-08-23 Intro to Data Science 15.593026 0.0 0.0
2308295 2015-08-23 880 1816-03-30 2015-08-23 Data Analysis with R 0.000000 0.0 0.0
2308296 2015-08-23 880 1816-03-30 2015-08-23 A/B Testing 0.000000 0.0 0.0
2308297 2015-08-23 880 1816-03-30 2015-08-23 Intro to Machine Learning 0.000000 0.0 0.0
2308298 2015-08-23 880 1816-03-30 2015-08-23 A/B Testing 0.000000 0.0 0.0
2308301 2015-08-23 880 1816-03-30 2015-08-23 JavaScript Basics 0.000000 0.0 0.0
2308302 2015-08-23 880 1816-03-30 2015-08-23 Intro to HTML and CSS 0.000000 0.0 0.0
2308303 2015-08-23 880 1816-03-30 2015-08-23 Data Wrangling with MongoDB 0.000000 0.0 0.0
2308304 2015-08-23 880 1816-03-30 2015-08-23 Intro to Data Science 0.000000 0.0 0.0
2308305 2015-08-23 880 1816-03-30 2015-08-23 Data Analysis with R 0.000000 0.0 0.0
2308306 2015-08-23 880 1816-03-30 2015-08-23 Data Visualization and D3.js 0.000000 0.0 0.0
2308307 2015-08-23 880 1816-03-30 2015-08-23 Data Analyst Nanodegree 0.000000 0.0 0.0
2308308 2015-08-24 880 1816-03-30 2015-08-23 Data Wrangling with MongoDB 0.000000 0.0 0.0
2308309 2015-08-24 880 1816-03-30 2015-08-23 Intro to Data Science 0.000000 0.0 0.0
2308310 2015-08-24 880 1816-03-30 2015-08-23 Data Analysis with R 0.000000 0.0 0.0
2308311 2015-08-24 880 1816-03-30 2015-08-23 JavaScript Basics 0.000000 0.0 0.0
2308312 2015-08-24 880 1816-03-30 2015-08-23 Data Visualization and D3.js 0.000000 0.0 0.0
2308313 2015-08-24 880 1816-03-30 2015-08-23 Intro to Machine Learning 0.000000 0.0 0.0
2308315 2015-08-24 880 1816-03-30 2015-08-23 A/B Testing 0.000000 0.0 0.0
2308317 2015-08-24 880 1816-03-30 2015-08-23 A/B Testing 0.000000 0.0 0.0
2308318 2015-08-24 880 1816-03-30 2015-08-23 Intro to HTML and CSS 0.000000 0.0 0.0
2308319 2015-08-24 880 1816-03-30 2015-08-23 Data Analysis with R 0.000000 0.0 0.0
2308320 2015-08-24 880 1816-03-30 2015-08-23 Intro to Machine Learning 0.000000 0.0 0.0
2308321 2015-08-24 880 1816-03-30 2015-08-23 Data Wrangling with MongoDB 0.000000 0.0 0.0
2308322 2015-08-24 880 1816-03-30 2015-08-23 Data Analyst Nanodegree 0.000000 0.0 0.0
2308323 2015-08-24 880 1816-03-30 2015-08-23 Data Visualization and D3.js 0.000000 0.0 0.0
2308324 2015-08-24 880 1816-03-30 2015-08-23 Intro to Data Science 0.000000 0.0 0.0
2308325 2015-08-24 880 1816-03-30 2015-08-23 Intro to HTML and CSS 0.000000 0.0 0.0
2308326 2015-08-24 880 1816-03-30 2015-08-23 JavaScript Basics 0.000000 0.0 0.0
Actually for 'registration_date' whrn year was 1816 there wasn't any technology yet, bad input so I'm going to drop it¶
In [1571]:
data.drop(year_1816.index, inplace=True)
In [1572]:
data
Out[1572]:
utc_date account_key registration_date subscription_start course_title total_minutes_visited lessons_completed projects_completed
0 2014-11-05 448 2014-08-05 2014-11-05 Intro to Data Science 0.0 0.0 0.0
1 2014-11-05 448 2014-08-05 2014-11-05 Intro to Machine Learning 0.0 0.0 0.0
2 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0
3 2014-11-05 448 2014-08-05 2014-11-05 Data Visualization and D3.js 0.0 0.0 0.0
4 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0
... ... ... ... ... ... ... ... ...
2309234 2015-08-26 854 2012-10-12 2015-08-23 Intro to Data Science 0.0 0.0 0.0
2309235 2015-08-26 854 2012-10-12 2015-08-23 JavaScript Basics 0.0 0.0 0.0
2309236 2015-08-26 854 2012-10-12 2015-08-23 Data Analysis with R 0.0 0.0 0.0
2309237 2015-08-26 854 2012-10-12 2015-08-23 Intro to HTML and CSS 0.0 0.0 0.0
2309238 2015-08-26 854 2012-10-12 2015-08-23 Data Analyst Nanodegree 0.0 0.0 0.0

2222076 rows × 8 columns

In [1573]:
pd.DatetimeIndex(data['registration_date']).year.unique()
Out[1573]:
Int64Index([2014, 2013, 2012, 2015], dtype='int64', name='registration_date')
In [1574]:
data['registration_date'][0].year
Out[1574]:
2014
In [1575]:
data['total_minutes_visited'][pd.DatetimeIndex(data['subscription_start']).year==2014].sum()
Out[1575]:
1273562.286094507
In [1576]:
data['total_minutes_visited'][pd.DatetimeIndex(data['subscription_start']).year==2015].sum()
Out[1576]:
1969273.1714284602
In [1577]:
data['total_minutes_visited'].sum()
Out[1577]:
3242835.457522968
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
Get common users for both subscription years: 2014, 2015¶
Also get the users that are related to each year¶
In [1578]:
acct_2014 = data['account_key'][pd.DatetimeIndex(data['subscription_start']).year==2014].unique()
In [1579]:
len(acct_2014)
Out[1579]:
429
In [1580]:
acct_2015 = data['account_key'][pd.DatetimeIndex(data['subscription_start']).year==2015].unique()
In [1581]:
len(acct_2015)
Out[1581]:
945
In [1582]:
len(data['account_key'].unique())
Out[1582]:
1237
In [1583]:
len(acct_2014) + len(acct_2015) == len(data['account_key'].unique())
Out[1583]:
False
In [1584]:
common_students = [x for x in acct_2014 if x in acct_2015]
In [1585]:
len(common_students)
Out[1585]:
137
In [1586]:
users_2014_only = [x for x in acct_2014 if x not in common_students]
In [1587]:
len(acct_2014_only)
Out[1587]:
292
In [1588]:
users_2015_only = [x for x in acct_2015 if x not in common_students]
In [1589]:
len(users_2015_only)
Out[1589]:
808
In [1590]:
len(common_students) + len(acct_2014_only) + len(users_2015_only) == len(data['account_key'].unique())
Out[1590]:
True

So now there's a three categories of students that can extract data according to¶

Some students doesn't achieve anything in the columns of 'total_minutes_visited', 'lessons_completed', 'projects_completed'¶

let's check which year have the highest number of inactive students¶

In [1595]:
lazy_acct_2014 = data['account_key'][(pd.DatetimeIndex(data['subscription_start']).year==2014) & (data['total_minutes_visited']==0) & (data['lessons_completed']==0) & (data['projects_completed']==0)].unique()
In [1596]:
len(lazy_acct_2014)
Out[1596]:
429
In [1597]:
lazy_acct_2015 = data['account_key'][(pd.DatetimeIndex(data['subscription_start']).year==2015) & (data['total_minutes_visited']==0) & (data['lessons_completed']==0) & (data['projects_completed']==0)].unique()
In [1598]:
len(lazy_acct_2015)
Out[1598]:
945
In [1599]:
list(lazy_acct_2014) == list(acct_2014)
Out[1599]:
True
In [1600]:
list(lazy_acct_2015) == list(acct_2015)
Out[1600]:
True

all the students are unstable for their nanodegrees material studying so we can neglect those data from the dataframe as it's huge and doesn't add any more information than that¶

In [1603]:
neglect = data.loc[(data['total_minutes_visited']==0) & (data['lessons_completed']==0) & (data['projects_completed']==0)]
In [1604]:
neglect
Out[1604]:
utc_date account_key registration_date subscription_start course_title total_minutes_visited lessons_completed projects_completed
0 2014-11-05 448 2014-08-05 2014-11-05 Intro to Data Science 0.0 0.0 0.0
1 2014-11-05 448 2014-08-05 2014-11-05 Intro to Machine Learning 0.0 0.0 0.0
2 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0
3 2014-11-05 448 2014-08-05 2014-11-05 Data Visualization and D3.js 0.0 0.0 0.0
4 2014-11-05 448 2014-08-05 2014-11-05 Data Analysis with R 0.0 0.0 0.0
... ... ... ... ... ... ... ... ...
2309234 2015-08-26 854 2012-10-12 2015-08-23 Intro to Data Science 0.0 0.0 0.0
2309235 2015-08-26 854 2012-10-12 2015-08-23 JavaScript Basics 0.0 0.0 0.0
2309236 2015-08-26 854 2012-10-12 2015-08-23 Data Analysis with R 0.0 0.0 0.0
2309237 2015-08-26 854 2012-10-12 2015-08-23 Intro to HTML and CSS 0.0 0.0 0.0
2309238 2015-08-26 854 2012-10-12 2015-08-23 Data Analyst Nanodegree 0.0 0.0 0.0

2176003 rows × 8 columns

In [1605]:
data.drop(neglect.index, inplace=True)
In [1614]:
data
Out[1614]:
utc_date account_key registration_date subscription_start course_title total_minutes_visited lessons_completed projects_completed
75 2014-11-10 448 2014-08-05 2014-11-10 Intro to Data Science 4.201422 0.0 0.0
80 2014-11-10 448 2014-08-05 2014-11-10 Intro to HTML and CSS 21.863585 0.0 0.0
89 2014-11-10 448 2014-08-05 2014-11-10 Data Analyst Nanodegree 4.681858 0.0 0.0
129 2014-11-13 448 2014-08-05 2014-11-10 Intro to Data Science 14.644702 0.0 0.0
132 2014-11-13 448 2014-08-05 2014-11-10 Data Visualization and D3.js 4.972973 0.0 0.0
... ... ... ... ... ... ... ... ...
2309085 2015-08-23 754 2015-01-22 2015-08-21 Intro to Machine Learning 23.399109 0.0 0.0
2309124 2015-08-25 754 2015-01-22 2015-08-21 Intro to Machine Learning 40.125426 0.0 0.0
2309142 2015-08-26 754 2015-01-22 2015-08-21 Intro to Machine Learning 68.243253 0.0 0.0
2309162 2015-08-27 754 2015-01-22 2015-08-21 Intro to Machine Learning 281.558425 2.0 0.0
2309215 2015-08-25 854 2012-10-12 2015-08-23 Data Analyst Nanodegree 22.077159 1.0 0.0

46073 rows × 8 columns

In [1615]:
pd.DatetimeIndex(data['subscription_start']).year.unique()
Out[1615]:
Int64Index([2014, 2015], dtype='int64', name='subscription_start')
In [1616]:
len(data['account_key'].unique())
Out[1616]:
1164

let's get students who subscribes in nanodegrees and not do any progress in it¶

In [1617]:
all_students = common_students + users_2014_only + users_2015_only
In [1618]:
good_students = list(data['account_key'].unique())
In [1619]:
bad_students = [u for u in all_students if u not in good_students]
In [1620]:
len(bad_students)
Out[1620]:
73
In [1613]:
bad_students
Out[1613]:
[837,
 1117,
 745,
 929,
 756,
 1114,
 930,
 683,
 1230,
 1239,
 1166,
 1203,
 865,
 767,
 1293,
 1234,
 907,
 790,
 867,
 1212,
 1074,
 1195,
 1092,
 1035,
 999,
 716,
 1045,
 1100,
 809,
 1066,
 899,
 1267,
 1088,
 729,
 1038,
 796,
 936,
 1265,
 1115,
 863,
 201,
 797,
 939,
 1275,
 1059,
 1192,
 1184,
 731,
 826,
 1266,
 1227,
 1172,
 978,
 951,
 915,
 1178,
 773,
 849,
 856,
 1302,
 879,
 1018,
 998,
 1136,
 816,
 840,
 1228,
 893,
 1028,
 1144,
 880,
 1157,
 874]

They are the 'account_key' for the users that didn't study anything or do any projects for any of the nanodegrees¶

In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [1621]:
data['subscription_start'][pd.DatetimeIndex(data['subscription_start']).year==2015].count()
Out[1621]:
27866
In [1622]:
# ['subscription_start', 'account_key', 'course_title', 'total_minutes_visited', 'lessons_completed', 'projects_completed']
In [1623]:
# to get the time spent per user for each of the courses across each year
def users_metadata(df, cols):
    total_yrs = {}
    lst_of_all_cols = []    # will be used in extraction of values from the df using regex
    
    for yr in pd.DatetimeIndex(df[cols[0]]).year.unique():
        total_all = {}
        print(f"\nfor year {yr}:")
        for acct in df[cols[1]].unique():
            courses_hrs = {}
            courses_lesn = {}
            courses_proj = {}
            total_hrs = 0
            for course in df[cols[2]].unique():
                c_hrs = course+'_hrs'
                c_lsns = course+'_lesson'
                c_proj = course+'_proj'
                
                mins = df[cols[3]][(pd.DatetimeIndex(df[cols[0]]).year==yr) & (df[cols[1]]==acct) & (df[cols[2]]==course)].sum()
                lessons = df[cols[4]][(pd.DatetimeIndex(df[cols[0]]).year==yr) & (df[cols[1]]==acct) & (df[cols[2]]==course)].sum()
                projects = df[cols[5]][(pd.DatetimeIndex(df[cols[0]]).year==yr) & (df[cols[1]]==acct) & (df[cols[2]]==course)].sum()
                courses_hrs[c_hrs] = mins/60
                courses_lesn[c_lsns] = lessons
                courses_proj[c_proj] = projects
                total_hrs += mins/60
        
            total_all[acct] = {cols[1]:acct, cols[0]:yr, "total_study_hrs":total_hrs, cols[4]:lessons, cols[5]:projects, 'c_hrs':courses_hrs, 'c_lsns':courses_lesn, 'c_proj':courses_proj}
            #print(total_all[acct])
            print(f"\tuser {acct} done...")
        total_yrs[yr] = total_all
    
    lst_of_all_cols.extend((cols[1], cols[0], "total_study_hrs", cols[4], cols[5]))
    lst_of_cols = [va for va in list(courses_hrs) + list(courses_lesn) + list(courses_proj)]
    lst_of_all_cols = lst_of_all_cols + lst_of_cols
        
    return total_yrs, lst_of_all_cols
In [1624]:
metadata, meta_cols = users_metadata(data, ['subscription_start', 'account_key', 'course_title', 'total_minutes_visited', 'lessons_completed', 'projects_completed'])
for year 2014:
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In [1625]:
len(metadata)
Out[1625]:
2
In [1626]:
len(meta_cols)
Out[1626]:
32
In [1627]:
meta_cols
Out[1627]:
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In [1628]:
metadata
Out[1628]:
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  128: {'account_key': 128,
   'subscription_start': 2015,
   'total_study_hrs': 107.6792813333495,
   'lessons_completed': 10.0,
   'projects_completed': 0.0,
   'c_hrs': {'Intro to Data Science_hrs': 20.336792797221666,
    'Intro to HTML and CSS_hrs': 0.0,
    'Data Analyst Nanodegree_hrs': 2.997663808338333,
    'Data Visualization and D3.js_hrs': 9.088740897233333,
    'Data Analysis with R_hrs': 23.651465780566664,
    'JavaScript Basics_hrs': 0.0,
    'Intro to Machine Learning_hrs': 27.09143693055,
    'A/B Testing_hrs': 0.0,
    'Data Wrangling with MongoDB_hrs': 24.513181119439498},
   'c_lsns': {'Intro to Data Science_lesson': 8.0,
    'Intro to HTML and CSS_lesson': 0.0,
    'Data Analyst Nanodegree_lesson': 1.0,
    'Data Visualization and D3.js_lesson': 4.0,
    'Data Analysis with R_lesson': 9.0,
    'JavaScript Basics_lesson': 0.0,
    'Intro to Machine Learning_lesson': 14.0,
    'A/B Testing_lesson': 0.0,
    'Data Wrangling with MongoDB_lesson': 10.0},
   'c_proj': {'Intro to Data Science_proj': 0.0,
    'Intro to HTML and CSS_proj': 0.0,
    'Data Analyst Nanodegree_proj': 0.0,
    'Data Visualization and D3.js_proj': 0.0,
    'Data Analysis with R_proj': 0.0,
    'JavaScript Basics_proj': 0.0,
    'Intro to Machine Learning_proj': 0.0,
    'A/B Testing_proj': 0.0,
    'Data Wrangling with MongoDB_proj': 0.0}},
  133: {'account_key': 133,
   'subscription_start': 2015,
   'total_study_hrs': 35.18656338333666,
   'lessons_completed': 5.0,
   'projects_completed': 0.0,
   'c_hrs': {'Intro to Data Science_hrs': 20.244442447233332,
    'Intro to HTML and CSS_hrs': 0.0,
    'Data Analyst Nanodegree_hrs': 2.967549355555,
    'Data Visualization and D3.js_hrs': 0.0,
    'Data Analysis with R_hrs': 1.983793799995,
    'JavaScript Basics_hrs': 0.0,
    'Intro to Machine Learning_hrs': 0.0,
    'A/B Testing_hrs': 0.0,
    'Data Wrangling with MongoDB_hrs': 9.990777780553334},
   'c_lsns': {'Intro to Data Science_lesson': 8.0,
    'Intro to HTML and CSS_lesson': 0.0,
    'Data Analyst Nanodegree_lesson': 1.0,
    'Data Visualization and D3.js_lesson': 0.0,
    'Data Analysis with R_lesson': 1.0,
    'JavaScript Basics_lesson': 0.0,
    'Intro to Machine Learning_lesson': 0.0,
    'A/B Testing_lesson': 0.0,
    'Data Wrangling with MongoDB_lesson': 5.0},
   'c_proj': {'Intro to Data Science_proj': 0.0,
    'Intro to HTML and CSS_proj': 0.0,
    'Data Analyst Nanodegree_proj': 0.0,
    'Data Visualization and D3.js_proj': 0.0,
    'Data Analysis with R_proj': 0.0,
    'JavaScript Basics_proj': 0.0,
    'Intro to Machine Learning_proj': 0.0,
    'A/B Testing_proj': 0.0,
    'Data Wrangling with MongoDB_proj': 0.0}},
  2: {'account_key': 2,
   'subscription_start': 2015,
   'total_study_hrs': 40.52917713612817,
   'lessons_completed': 1.0,
   'projects_completed': 0.0,
   'c_hrs': {'Intro to Data Science_hrs': 27.839220022237168,
    'Intro to HTML and CSS_hrs': 0.0,
    'Data Analyst Nanodegree_hrs': 1.1035627611115002,
    'Data Visualization and D3.js_hrs': 0.0,
    'Data Analysis with R_hrs': 0.0,
    'JavaScript Basics_hrs': 0.0,
    'Intro to Machine Learning_hrs': 0.20560130277833333,
    'A/B Testing_hrs': 0.0,
    'Data Wrangling with MongoDB_hrs': 11.380793050001166},
   'c_lsns': {'Intro to Data Science_lesson': 6.0,
    'Intro to HTML and CSS_lesson': 0.0,
    'Data Analyst Nanodegree_lesson': 0.0,
    'Data Visualization and D3.js_lesson': 0.0,
    'Data Analysis with R_lesson': 0.0,
    'JavaScript Basics_lesson': 0.0,
    'Intro to Machine Learning_lesson': 0.0,
    'A/B Testing_lesson': 0.0,
    'Data Wrangling with MongoDB_lesson': 1.0},
   'c_proj': {'Intro to Data Science_proj': 0.0,
    'Intro to HTML and CSS_proj': 0.0,
    'Data Analyst Nanodegree_proj': 0.0,
    'Data Visualization and D3.js_proj': 0.0,
    'Data Analysis with R_proj': 0.0,
    'JavaScript Basics_proj': 0.0,
    'Intro to Machine Learning_proj': 0.0,
    'A/B Testing_proj': 0.0,
    'Data Wrangling with MongoDB_proj': 0.0}},
  961: {'account_key': 961,
   'subscription_start': 2015,
   'total_study_hrs': 0.27819745277833335,
   'lessons_completed': 0.0,
   'projects_completed': 0.0,
   'c_hrs': {'Intro to Data Science_hrs': 0.0,
    'Intro to HTML and CSS_hrs': 0.0,
    'Data Analyst Nanodegree_hrs': 0.27819745277833335,
    'Data Visualization and D3.js_hrs': 0.0,
    'Data Analysis with R_hrs': 0.0,
    'JavaScript Basics_hrs': 0.0,
    'Intro to Machine Learning_hrs': 0.0,
    'A/B Testing_hrs': 0.0,
    'Data Wrangling with MongoDB_hrs': 0.0},
   'c_lsns': {'Intro to Data Science_lesson': 0.0,
    'Intro to HTML and CSS_lesson': 0.0,
    'Data Analyst Nanodegree_lesson': 0.0,
    'Data Visualization and D3.js_lesson': 0.0,
    'Data Analysis with R_lesson': 0.0,
    'JavaScript Basics_lesson': 0.0,
    'Intro to Machine Learning_lesson': 0.0,
    'A/B Testing_lesson': 0.0,
    'Data Wrangling with MongoDB_lesson': 0.0},
   'c_proj': {'Intro to Data Science_proj': 0.0,
    'Intro to HTML and CSS_proj': 0.0,
    'Data Analyst Nanodegree_proj': 0.0,
    'Data Visualization and D3.js_proj': 0.0,
    'Data Analysis with R_proj': 0.0,
    'JavaScript Basics_proj': 0.0,
    'Intro to Machine Learning_proj': 0.0,
    'A/B Testing_proj': 0.0,
    'Data Wrangling with MongoDB_proj': 0.0}},
  956: {'account_key': 956,
   'subscription_start': 2015,
   'total_study_hrs': 0.11344804722216667,
   'lessons_completed': 0.0,
   'projects_completed': 0.0,
   'c_hrs': {'Intro to Data Science_hrs': 0.0,
    'Intro to HTML and CSS_hrs': 0.0,
    'Data Analyst Nanodegree_hrs': 0.11344804722216667,
    'Data Visualization and D3.js_hrs': 0.0,
    'Data Analysis with R_hrs': 0.0,
    'JavaScript Basics_hrs': 0.0,
    'Intro to Machine Learning_hrs': 0.0,
    'A/B Testing_hrs': 0.0,
    'Data Wrangling with MongoDB_hrs': 0.0},
   'c_lsns': {'Intro to Data Science_lesson': 0.0,
    'Intro to HTML and CSS_lesson': 0.0,
    'Data Analyst Nanodegree_lesson': 0.0,
    'Data Visualization and D3.js_lesson': 0.0,
    'Data Analysis with R_lesson': 0.0,
    'JavaScript Basics_lesson': 0.0,
    'Intro to Machine Learning_lesson': 0.0,
    'A/B Testing_lesson': 0.0,
    'Data Wrangling with MongoDB_lesson': 0.0},
   'c_proj': {'Intro to Data Science_proj': 0.0,
    'Intro to HTML and CSS_proj': 0.0,
    'Data Analyst Nanodegree_proj': 0.0,
    'Data Visualization and D3.js_proj': 0.0,
    'Data Analysis with R_proj': 0.0,
    'JavaScript Basics_proj': 0.0,
    'Intro to Machine Learning_proj': 0.0,
    'A/B Testing_proj': 0.0,
    'Data Wrangling with MongoDB_proj': 0.0}},
  ...}}
In [1634]:
metadata[2014][160]['c_proj']
Out[1634]:
{'Intro to Data Science_proj': 1.0,
 'Intro to HTML and CSS_proj': 0.0,
 'Data Analyst Nanodegree_proj': 0.0,
 'Data Visualization and D3.js_proj': 0.0,
 'Data Analysis with R_proj': 0.0,
 'JavaScript Basics_proj': 0.0,
 'Intro to Machine Learning_proj': 0.0,
 'A/B Testing_proj': 0.0,
 'Data Wrangling with MongoDB_proj': 0.0}
In [1635]:
metadata[2014][50]['c_proj'] == metadata[2015][50]['c_proj']
Out[1635]:
False
In [1636]:
metadata[2014][50] == metadata[2015][50]
Out[1636]:
False
In [1637]:
users_study_df = pd.DataFrame.from_dict(metadata, orient='index').T
In [1638]:
users_study_df
Out[1638]:
2014 2015
448 {'account_key': 448, 'subscription_start': 201... {'account_key': 448, 'subscription_start': 201...
44 {'account_key': 44, 'subscription_start': 2014... {'account_key': 44, 'subscription_start': 2015...
258 {'account_key': 258, 'subscription_start': 201... {'account_key': 258, 'subscription_start': 201...
366 {'account_key': 366, 'subscription_start': 201... {'account_key': 366, 'subscription_start': 201...
587 {'account_key': 587, 'subscription_start': 201... {'account_key': 587, 'subscription_start': 201...
... ... ...
990 {'account_key': 990, 'subscription_start': 201... {'account_key': 990, 'subscription_start': 201...
686 {'account_key': 686, 'subscription_start': 201... {'account_key': 686, 'subscription_start': 201...
1067 {'account_key': 1067, 'subscription_start': 20... {'account_key': 1067, 'subscription_start': 20...
754 {'account_key': 754, 'subscription_start': 201... {'account_key': 754, 'subscription_start': 201...
854 {'account_key': 854, 'subscription_start': 201... {'account_key': 854, 'subscription_start': 201...

1164 rows × 2 columns

In [1639]:
users_study_df.to_csv('cleaned_daily_engagement.csv')
In [1640]:
users_study_df.columns
Out[1640]:
Int64Index([2014, 2015], dtype='int64')
In [1641]:
users_study_df[2014][130]
Out[1641]:
{'account_key': 130,
 'subscription_start': 2014,
 'total_study_hrs': 86.96879140835233,
 'lessons_completed': 8.0,
 'projects_completed': 0.0,
 'c_hrs': {'Intro to Data Science_hrs': 53.17091580556167,
  'Intro to HTML and CSS_hrs': 0.0,
  'Data Analyst Nanodegree_hrs': 3.4385773888845006,
  'Data Visualization and D3.js_hrs': 0.0,
  'Data Analysis with R_hrs': 0.0,
  'JavaScript Basics_hrs': 0.0,
  'Intro to Machine Learning_hrs': 0.0,
  'A/B Testing_hrs': 0.0,
  'Data Wrangling with MongoDB_hrs': 30.359298213906165},
 'c_lsns': {'Intro to Data Science_lesson': 9.0,
  'Intro to HTML and CSS_lesson': 0.0,
  'Data Analyst Nanodegree_lesson': 0.0,
  'Data Visualization and D3.js_lesson': 0.0,
  'Data Analysis with R_lesson': 0.0,
  'JavaScript Basics_lesson': 0.0,
  'Intro to Machine Learning_lesson': 0.0,
  'A/B Testing_lesson': 0.0,
  'Data Wrangling with MongoDB_lesson': 8.0},
 'c_proj': {'Intro to Data Science_proj': 1.0,
  'Intro to HTML and CSS_proj': 0.0,
  'Data Analyst Nanodegree_proj': 0.0,
  'Data Visualization and D3.js_proj': 0.0,
  'Data Analysis with R_proj': 0.0,
  'JavaScript Basics_proj': 0.0,
  'Intro to Machine Learning_proj': 0.0,
  'A/B Testing_proj': 0.0,
  'Data Wrangling with MongoDB_proj': 0.0}}
In [1642]:
#users_study_df = users_study_df.astype(str)
users_study_df.dtypes
Out[1642]:
2014    object
2015    object
dtype: object

We can now use regular expression in python to extract the wanted data¶

In [1645]:
import re
In [1646]:
def regex_df(df, new_cols):
    lst_of_dfs = []
    
    for c in df.columns:
        df_lst = []
        #df_new = pd.DataFrame(columns=new_cols)
        
        for row in df[c]:
            spare = []
            
            for col in new_cols:
                to_extract = re.compile(f"'{col}': [0-9]+((.[0-9])?)*")   # (?s:.*?)   # .[0-9]?
                #to_extract = re.match(col, str(row))
                compared = to_extract.search(str(row))
                if compared!=None:
                    spare.append(str(compared.group()[len(col)+4:]))
                else:
                    spare.append(None)
            
            df_lst.append(spare)
        
        df_new = pd.DataFrame(df_lst, columns=new_cols)
        df_new.to_csv(f'daily_engagement_{c}.csv')
        print(f"daily_engagement_{c} has been saved....")
        lst_of_dfs.append(df_new)
    return lst_of_dfs
In [1647]:
each_year_user_lst_df = regex_df(users_study_df, meta_cols)
daily_engagement_2014 has been saved....
daily_engagement_2015 has been saved....
In [1648]:
each_year_user_lst_df
Out[1648]:
[     account_key subscription_start     total_study_hrs lessons_completed  \
 0            448               2014   7.865357536110332               0.0   
 1             44               2014  46.394156652769325              12.0   
 2            258               2014      99.50050039167              11.0   
 3            366               2014   102.5816878972188              11.0   
 4            587               2014  17.879348477769334               0.0   
 ...          ...                ...                 ...               ...   
 1159         990               2014                 0.0               0.0   
 1160         686               2014                 0.0               0.0   
 1161        1067               2014                 0.0               0.0   
 1162         754               2014                 0.0               0.0   
 1163         854               2014                 0.0               0.0   
 
      projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs  \
 0                   0.0         1.630967122222833             0.76440243055   
 1                   0.0            22.17087966667                       0.0   
 2                   1.0        22.683015541666666                       0.0   
 3                   0.0          33.8699802444555                       0.0   
 4                   0.0        16.004607786103833                       0.0   
 ...                 ...                       ...                       ...   
 1159                0.0                       0.0                       0.0   
 1160                0.0                       0.0                       0.0   
 1161                0.0                       0.0                       0.0   
 1162                0.0                       0.0                       0.0   
 1163                0.0                       0.0                       0.0   
 
      Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs  \
 0              3.406064944444332              0.15292225277766666   
 1              2.638166563883333                              0.0   
 2              5.058334588888333                9.445516413890001   
 3              2.968250669443833                3.994799516661166   
 4              1.617224061110500                              0.0   
 ...                          ...                              ...   
 1159                         0.0                              0.0   
 1160                         0.0                              0.0   
 1161                         0.0                              0.0   
 1162                         0.0                              0.0   
 1163                         0.0                              0.0   
 
      Data Analysis with R_hrs  ... Data Wrangling with MongoDB_lesson  \
 0         0.35808756388833335  ...                                0.0   
 1                         0.0  ...                               12.0   
 2            19.8368701444333  ...                               11.0   
 3           9.556285272220002  ...                               11.0   
 4                         0.0  ...                                0.0   
 ...                       ...  ...                                ...   
 1159                      0.0  ...                                0.0   
 1160                      0.0  ...                                0.0   
 1161                      0.0  ...                                0.0   
 1162                      0.0  ...                                0.0   
 1163                      0.0  ...                                0.0   
 
      Intro to Data Science_proj Intro to HTML and CSS_proj  \
 0                           0.0                        0.0   
 1                           1.0                        0.0   
 2                           1.0                        0.0   
 3                           1.0                        0.0   
 4                           0.0                        0.0   
 ...                         ...                        ...   
 1159                        0.0                        0.0   
 1160                        0.0                        0.0   
 1161                        0.0                        0.0   
 1162                        0.0                        0.0   
 1163                        0.0                        0.0   
 
      Data Analyst Nanodegree_proj Data Visualization and D3.js_proj  \
 0                             0.0                               0.0   
 1                             0.0                               0.0   
 2                             1.0                               1.0   
 3                             0.0                               0.0   
 4                             0.0                               0.0   
 ...                           ...                               ...   
 1159                          0.0                               0.0   
 1160                          0.0                               0.0   
 1161                          0.0                               0.0   
 1162                          0.0                               0.0   
 1163                          0.0                               0.0   
 
      Data Analysis with R_proj JavaScript Basics_proj  \
 0                          0.0                    0.0   
 1                          0.0                    0.0   
 2                          1.0                    0.0   
 3                          0.0                    0.0   
 4                          0.0                    0.0   
 ...                        ...                    ...   
 1159                       0.0                    0.0   
 1160                       0.0                    0.0   
 1161                       0.0                    0.0   
 1162                       0.0                    0.0   
 1163                       0.0                    0.0   
 
      Intro to Machine Learning_proj A/B Testing_proj  \
 0                               0.0              0.0   
 1                               0.0              0.0   
 2                               1.0              0.0   
 3                               0.0              0.0   
 4                               0.0              0.0   
 ...                             ...              ...   
 1159                            0.0              0.0   
 1160                            0.0              0.0   
 1161                            0.0              0.0   
 1162                            0.0              0.0   
 1163                            0.0              0.0   
 
      Data Wrangling with MongoDB_proj  
 0                                 0.0  
 1                                 0.0  
 2                                 1.0  
 3                                 0.0  
 4                                 0.0  
 ...                               ...  
 1159                              0.0  
 1160                              0.0  
 1161                              0.0  
 1162                              0.0  
 1163                              0.0  
 
 [1164 rows x 32 columns],
      account_key subscription_start      total_study_hrs lessons_completed  \
 0            448               2015   13.087178891668332               0.0   
 1             44               2015       24.78622549443               0.0   
 2            258               2015                  0.0               0.0   
 3            366               2015    3.230152600000166               0.0   
 4            587               2015     41.0999053833245               5.0   
 ...          ...                ...                  ...               ...   
 1159         990               2015    0.836528125001166               0.0   
 1160         686               2015  0.16020183888883333               0.0   
 1161        1067               2015    0.495628972221666               0.0   
 1162         754               2015    6.888770208328333               0.0   
 1163         854               2015  0.36795265277833333               0.0   
 
      projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs  \
 0                   0.0       0.05284328333333333         0.332654547222166   
 1                   1.0                       0.0                       0.0   
 2                   0.0                       0.0                       0.0   
 3                   0.0         1.925212513889999                       0.0   
 4                   0.0        28.539121397216164                       0.0   
 ...                 ...                       ...                       ...   
 1159                0.0                       0.0                       0.0   
 1160                0.0                       0.0                       0.0   
 1161                0.0                       0.0                       0.0   
 1162                0.0                       0.0                       0.0   
 1163                0.0                       0.0                       0.0   
 
      Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs  \
 0               10.1521735861133                1.096988658332333   
 1              1.376195825001666              0.04586874166666666   
 2                            0.0                              0.0   
 3            0.30557437222166667                              0.0   
 4              1.250586677776666                              0.0   
 ...                          ...                              ...   
 1159           0.836528125001166                              0.0   
 1160         0.16020183888883333                              0.0   
 1161           0.495628972221666                              0.0   
 1162                         0.0                              0.0   
 1163         0.36795265277833333                              0.0   
 
      Data Analysis with R_hrs  ... Data Wrangling with MongoDB_lesson  \
 0         0.42080968611166664  ...                                0.0   
 1          15.791170974987834  ...                                0.0   
 2                         0.0  ...                                0.0   
 3                         0.0  ...                                0.0   
 4                         0.0  ...                                5.0   
 ...                       ...  ...                                ...   
 1159                      0.0  ...                                0.0   
 1160                      0.0  ...                                0.0   
 1161                      0.0  ...                                0.0   
 1162                      0.0  ...                                0.0   
 1163                      0.0  ...                                0.0   
 
      Intro to Data Science_proj Intro to HTML and CSS_proj  \
 0                           1.0                        0.0   
 1                           0.0                        0.0   
 2                           0.0                        0.0   
 3                           0.0                        0.0   
 4                           0.0                        0.0   
 ...                         ...                        ...   
 1159                        0.0                        0.0   
 1160                        0.0                        0.0   
 1161                        0.0                        0.0   
 1162                        0.0                        0.0   
 1163                        0.0                        0.0   
 
      Data Analyst Nanodegree_proj Data Visualization and D3.js_proj  \
 0                             0.0                               0.0   
 1                             0.0                               0.0   
 2                             0.0                               0.0   
 3                             0.0                               0.0   
 4                             0.0                               0.0   
 ...                           ...                               ...   
 1159                          0.0                               0.0   
 1160                          0.0                               0.0   
 1161                          0.0                               0.0   
 1162                          0.0                               0.0   
 1163                          0.0                               0.0   
 
      Data Analysis with R_proj JavaScript Basics_proj  \
 0                          0.0                    0.0   
 1                          1.0                    0.0   
 2                          0.0                    0.0   
 3                          0.0                    0.0   
 4                          0.0                    0.0   
 ...                        ...                    ...   
 1159                       0.0                    0.0   
 1160                       0.0                    0.0   
 1161                       0.0                    0.0   
 1162                       0.0                    0.0   
 1163                       0.0                    0.0   
 
      Intro to Machine Learning_proj A/B Testing_proj  \
 0                               0.0              0.0   
 1                               0.0              0.0   
 2                               0.0              0.0   
 3                               0.0              0.0   
 4                               0.0              0.0   
 ...                             ...              ...   
 1159                            0.0              0.0   
 1160                            0.0              0.0   
 1161                            0.0              0.0   
 1162                            0.0              0.0   
 1163                            0.0              0.0   
 
      Data Wrangling with MongoDB_proj  
 0                                 0.0  
 1                                 1.0  
 2                                 0.0  
 3                                 0.0  
 4                                 0.0  
 ...                               ...  
 1159                              0.0  
 1160                              0.0  
 1161                              0.0  
 1162                              0.0  
 1163                              0.0  
 
 [1164 rows x 32 columns]]

Now we have two .csv files contains the student progress per each year starting from the subscribtion date¶

In [1649]:
len(acct_2014)
Out[1649]:
429
In [1650]:
len(acct_2015)
Out[1650]:
945
In [1651]:
len(common_students)
Out[1651]:
137
Actually there's a little problem, which is the csv files that are created for students contains all of them for both years, and that's not true, it's not a real problem as the data filled for those students that didn't achieve any progress during the year as zeros for all columns, but if we remove them we'll have a more cleaned data, so it's optional, and I'm going to remove it and save my data once again without any overwritting¶
In [1652]:
df_2014 = pd.read_csv("daily_engagement_2014.csv")
In [1653]:
df_2014
Out[1653]:
Unnamed: 0 account_key subscription_start total_study_hrs lessons_completed projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs ... Data Wrangling with MongoDB_lesson Intro to Data Science_proj Intro to HTML and CSS_proj Data Analyst Nanodegree_proj Data Visualization and D3.js_proj Data Analysis with R_proj JavaScript Basics_proj Intro to Machine Learning_proj A/B Testing_proj Data Wrangling with MongoDB_proj
0 0 448 2014 7.865358 0.0 0.0 1.630967 0.764402 3.406065 0.152922 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 1 44 2014 46.394157 12.0 0.0 22.170880 0.000000 2.638167 0.000000 ... 12.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 2 258 2014 99.500500 11.0 1.0 22.683016 0.000000 5.058335 9.445516 ... 11.0 1.0 0.0 1.0 1.0 1.0 0.0 1.0 0.0 1.0
3 3 366 2014 102.581688 11.0 0.0 33.869980 0.000000 2.968251 3.994800 ... 11.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 4 587 2014 17.879348 0.0 0.0 16.004608 0.000000 1.617224 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1159 1159 990 2014 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1160 1160 686 2014 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1161 1161 1067 2014 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1162 1162 754 2014 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1163 1163 854 2014 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1164 rows × 33 columns

In [1654]:
df_2015 = pd.read_csv("daily_engagement_2015.csv")
In [1655]:
df_2015
Out[1655]:
Unnamed: 0 account_key subscription_start total_study_hrs lessons_completed projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs ... Data Wrangling with MongoDB_lesson Intro to Data Science_proj Intro to HTML and CSS_proj Data Analyst Nanodegree_proj Data Visualization and D3.js_proj Data Analysis with R_proj JavaScript Basics_proj Intro to Machine Learning_proj A/B Testing_proj Data Wrangling with MongoDB_proj
0 0 448 2015 13.087179 0.0 0.0 0.052843 0.332655 10.152174 1.096989 ... 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 1 44 2015 24.786225 0.0 1.0 0.000000 0.000000 1.376196 0.045869 ... 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0
2 2 258 2015 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
3 3 366 2015 3.230153 0.0 0.0 1.925213 0.000000 0.305574 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 4 587 2015 41.099905 5.0 0.0 28.539121 0.000000 1.250587 0.000000 ... 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1159 1159 990 2015 0.836528 0.0 0.0 0.000000 0.000000 0.836528 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1160 1160 686 2015 0.160202 0.0 0.0 0.000000 0.000000 0.160202 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1161 1161 1067 2015 0.495629 0.0 0.0 0.000000 0.000000 0.495629 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1162 1162 754 2015 6.888770 0.0 0.0 0.000000 0.000000 0.000000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1163 1163 854 2015 0.367953 0.0 0.0 0.000000 0.000000 0.367953 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

1164 rows × 33 columns

In [1656]:
df_2014['account_key'].tolist() == df_2015['account_key'].tolist()
Out[1656]:
True
In [1657]:
unwanted_for_2014 = [x for x in acct_2015 if x not in acct_2014]
In [1658]:
len(unwanted_for_2014)
Out[1658]:
808
In [1659]:
len(unwanted_for_2014) + len(common_students) == len(acct_2015)
Out[1659]:
True
In [1660]:
unwanted_for_2015 = [x for x in acct_2014 if x not in acct_2015]
In [1661]:
len(unwanted_for_2015)
Out[1661]:
292
In [1662]:
len(unwanted_for_2015) + len(common_students) == len(acct_2014)
Out[1662]:
True
In [1663]:
len(unwanted_for_2014) + len(common_students) == len(acct_2015)
Out[1663]:
True
In [1664]:
neglect_2014 = df_2014[df_2014['account_key'].isin(unwanted_for_2014)]
In [1665]:
neglect_2014
Out[1665]:
Unnamed: 0 account_key subscription_start total_study_hrs lessons_completed projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs ... Data Wrangling with MongoDB_lesson Intro to Data Science_proj Intro to HTML and CSS_proj Data Analyst Nanodegree_proj Data Visualization and D3.js_proj Data Analysis with R_proj JavaScript Basics_proj Intro to Machine Learning_proj A/B Testing_proj Data Wrangling with MongoDB_proj
410 410 460 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
411 411 108 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
412 412 197 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
413 413 442 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
414 414 1283 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1159 1159 990 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1160 1160 686 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1161 1161 1067 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1162 1162 754 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1163 1163 854 2014 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

754 rows × 33 columns

In [1666]:
df_2014.drop(neglect_2014.index, inplace=True)
In [1667]:
df_2014
Out[1667]:
Unnamed: 0 account_key subscription_start total_study_hrs lessons_completed projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs ... Data Wrangling with MongoDB_lesson Intro to Data Science_proj Intro to HTML and CSS_proj Data Analyst Nanodegree_proj Data Visualization and D3.js_proj Data Analysis with R_proj JavaScript Basics_proj Intro to Machine Learning_proj A/B Testing_proj Data Wrangling with MongoDB_proj
0 0 448 2014 7.865358 0.0 0.0 1.630967 0.764402 3.406065 0.152922 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 1 44 2014 46.394157 12.0 0.0 22.170880 0.000000 2.638167 0.000000 ... 12.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
2 2 258 2014 99.500500 11.0 1.0 22.683016 0.000000 5.058335 9.445516 ... 11.0 1.0 0.0 1.0 1.0 1.0 0.0 1.0 0.0 1.0
3 3 366 2014 102.581688 11.0 0.0 33.869980 0.000000 2.968251 3.994800 ... 11.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 4 587 2014 17.879348 0.0 0.0 16.004608 0.000000 1.617224 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
405 405 987 2014 1.060631 0.0 0.0 0.000000 0.000000 0.217330 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
406 406 917 2014 0.612932 0.0 0.0 0.506507 0.000000 0.106425 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
407 407 474 2014 74.349377 11.0 1.0 15.806123 2.393722 3.264829 7.740320 ... 11.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 1.0
408 408 634 2014 52.854911 11.0 1.0 20.995900 0.000000 1.178572 0.000000 ... 11.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0
409 409 319 2014 18.552699 1.0 0.0 10.557275 0.000000 3.717940 0.151121 ... 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

410 rows × 33 columns

In [1669]:
df_2014.to_csv("cleaned_2014.csv")
In [ ]:
 
In [ ]:
 
In [1670]:
neglect_2015 = df_2015[df_2015['account_key'].isin(unwanted_for_2015)]
In [1671]:
df_2015.drop(neglect_2015.index, inplace=True)
In [1672]:
df_2015
Out[1672]:
Unnamed: 0 account_key subscription_start total_study_hrs lessons_completed projects_completed Intro to Data Science_hrs Intro to HTML and CSS_hrs Data Analyst Nanodegree_hrs Data Visualization and D3.js_hrs ... Data Wrangling with MongoDB_lesson Intro to Data Science_proj Intro to HTML and CSS_proj Data Analyst Nanodegree_proj Data Visualization and D3.js_proj Data Analysis with R_proj JavaScript Basics_proj Intro to Machine Learning_proj A/B Testing_proj Data Wrangling with MongoDB_proj
0 0 448 2015 13.087179 0.0 0.0 0.052843 0.332655 10.152174 1.096989 ... 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1 1 44 2015 24.786225 0.0 1.0 0.000000 0.000000 1.376196 0.045869 ... 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 1.0
3 3 366 2015 3.230153 0.0 0.0 1.925213 0.000000 0.305574 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
4 4 587 2015 41.099905 5.0 0.0 28.539121 0.000000 1.250587 0.000000 ... 5.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
6 6 412 2015 3.528850 0.0 0.0 0.137447 0.000000 0.407035 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
1159 1159 990 2015 0.836528 0.0 0.0 0.000000 0.000000 0.836528 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1160 1160 686 2015 0.160202 0.0 0.0 0.000000 0.000000 0.160202 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1161 1161 1067 2015 0.495629 0.0 0.0 0.000000 0.000000 0.495629 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1162 1162 754 2015 6.888770 0.0 0.0 0.000000 0.000000 0.000000 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
1163 1163 854 2015 0.367953 0.0 0.0 0.000000 0.000000 0.367953 0.000000 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

891 rows × 33 columns

In [1673]:
df_2015.to_csv("cleaned_2015.csv")
In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]:
 

Now let's plot some plots for the students according to their achievements in both years¶

In [1674]:
cleaned_2014 = pd.read_csv('cleaned_2014.csv')
cleaned_2015 = pd.read_csv('cleaned_2015.csv')
In [1675]:
len(cleaned_2015.columns)
Out[1675]:
34
In [1676]:
len(cleaned_2014.columns)
Out[1676]:
34
In [1678]:
to_plot_hrs = list(cleaned_2015.columns)[7:16]
In [1679]:
to_plot_hrs
Out[1679]:
['Intro to Data Science_hrs',
 'Intro to HTML and CSS_hrs',
 'Data Analyst Nanodegree_hrs',
 'Data Visualization and D3.js_hrs',
 'Data Analysis with R_hrs',
 'JavaScript Basics_hrs',
 'Intro to Machine Learning_hrs',
 'A/B Testing_hrs',
 'Data Wrangling with MongoDB_hrs']
In [1680]:
to_plot_lsns = list(cleaned_2015.columns)[16:25]
In [1681]:
to_plot_lsns
Out[1681]:
['Intro to Data Science_lesson',
 'Intro to HTML and CSS_lesson',
 'Data Analyst Nanodegree_lesson',
 'Data Visualization and D3.js_lesson',
 'Data Analysis with R_lesson',
 'JavaScript Basics_lesson',
 'Intro to Machine Learning_lesson',
 'A/B Testing_lesson',
 'Data Wrangling with MongoDB_lesson']
In [1682]:
to_plot_projs = list(cleaned_2015.columns)[25:]
In [1683]:
to_plot_projs
Out[1683]:
['Intro to Data Science_proj',
 'Intro to HTML and CSS_proj',
 'Data Analyst Nanodegree_proj',
 'Data Visualization and D3.js_proj',
 'Data Analysis with R_proj',
 'JavaScript Basics_proj',
 'Intro to Machine Learning_proj',
 'A/B Testing_proj',
 'Data Wrangling with MongoDB_proj']

plot the data according to the studied hours, completed_lessons and completed_projects per each nanodegree to get the most five students per each nanodegree

In [1699]:
def plot_progress(df, col1, cols_lst_hrs, cols_lst_lsns, cols_lst_proj, num):
    numbr = [i+1 for i in range(len(cols_lst_hrs))]
    for hrs, lsns, proj, n in zip(cols_lst_hrs, cols_lst_lsns, cols_lst_proj, numbr):
        fig, axes = plt.subplots(1, 3, figsize=(11, 5))
        title = hrs.split('_')[0]
        fig.suptitle(title, fontsize=16)
        
        df_hrs = df.sort_values(by=[hrs])
        df_lsns = df.sort_values(by=[lsns])
        df_proj = df.sort_values(by=[proj])
        
        toPlot_hrs = df_hrs[hrs].tail(num)
        toPlot_lsns = df_lsns[lsns].tail(num)
        toPlot_proj = df_proj[proj].tail(num)
        
        toPlot_hrs.plot(ax=axes[0], kind='bar', title=title, figsize=(10, 6))
        toPlot_lsns.plot(ax=axes[1], kind='bar', title=title, figsize=(10, 6))
        toPlot_proj.plot(ax=axes[2], kind='bar', title=title, figsize=(10, 6))
        
        axes[0].set_title(hrs.split('_')[1])
        axes[1].set_title(lsns.split('_')[1])
        axes[2].set_title(proj.split('_')[1])
        plt.savefig(f'{n}.png', facecolor='w', bbox_inches="tight", pad_inches=0.3, transparent=False)
        plt.show()
        #plt.xlabel(col1)
        #plt.ylabel(col2)
        #plt.show()
        print(toPlot_hrs, toPlot_lsns, toPlot_proj)
In [1700]:
plot_progress(cleaned_2014, 'account_key', to_plot_hrs, to_plot_lsns, to_plot_projs, 5)
122    72.939193
216    75.020808
71     83.520889
74     85.327424
241    87.144486
Name: Intro to Data Science_hrs, dtype: float64 16     10.0
335    10.0
282    10.0
304    10.0
260    10.0
Name: Intro to Data Science_lesson, dtype: float64 261    1.0
263    1.0
264    1.0
100    1.0
82     1.0
Name: Intro to Data Science_proj, dtype: float64
158    5.808720
102    6.232276
48     7.137908
347    7.861887
121    8.349262
Name: Intro to HTML and CSS_hrs, dtype: float64 48     3.0
154    3.0
343    3.0
117    3.0
100    3.0
Name: Intro to HTML and CSS_lesson, dtype: float64 130    0.0
140    0.0
409    0.0
48     1.0
407    1.0
Name: Intro to HTML and CSS_proj, dtype: float64
207     9.080338
360     9.154015
91      9.258947
42     10.848733
237    10.874616
Name: Data Analyst Nanodegree_hrs, dtype: float64 91     2.0
264    2.0
13     2.0
360    3.0
237    3.0
Name: Data Analyst Nanodegree_lesson, dtype: float64 42     1.0
264    1.0
158    1.0
121    1.0
154    1.0
Name: Data Analyst Nanodegree_proj, dtype: float64
121    28.997682
328    29.093131
115    34.349993
216    34.425644
101    41.181727
Name: Data Visualization and D3.js_hrs, dtype: float64 24     8.0
38     8.0
97     8.0
268    8.0
77     8.0
Name: Data Visualization and D3.js_lesson, dtype: float64 89     1.0
268    1.0
121    1.0
22     1.0
77     1.0
Name: Data Visualization and D3.js_proj, dtype: float64
108    50.604694
216    52.406257
29     58.416228
308    61.452862
93     64.785145
Name: Data Analysis with R_hrs, dtype: float64 207    9.0
303    9.0
77     9.0
201    9.0
286    9.0
Name: Data Analysis with R_lesson, dtype: float64 107    1.0
108    1.0
111    1.0
306    1.0
94     1.0
Name: Data Analysis with R_proj, dtype: float64
324    5.920621
48     6.367822
158    7.172721
24     7.707952
121    8.310206
Name: JavaScript Basics_hrs, dtype: float64 24     6.0
324    6.0
343    7.0
48     7.0
154    7.0
Name: JavaScript Basics_lesson, dtype: float64 130    0.0
140    0.0
409    0.0
48     1.0
407    1.0
Name: JavaScript Basics_proj, dtype: float64
42     55.472784
121    57.905742
93     76.009287
216    89.215894
308    93.494506
Name: Intro to Machine Learning_hrs, dtype: float64 100    14.0
101    14.0
102    14.0
308    14.0
93     14.0
Name: Intro to Machine Learning_lesson, dtype: float64 89     1.0
264    1.0
50     1.0
42     1.0
115    1.0
Name: Intro to Machine Learning_proj, dtype: float64
24      8.757659
74     10.734904
107    11.776014
102    16.290011
101    22.784276
Name: A/B Testing_hrs, dtype: float64 347    4.0
101    4.0
24     4.0
207    5.0
102    5.0
Name: A/B Testing_lesson, dtype: float64 132    0.0
131    0.0
130    0.0
140    0.0
409    0.0
Name: A/B Testing_proj, dtype: float64
201     64.806271
358     75.069440
29      94.013464
308     94.702774
74     115.771617
Name: Data Wrangling with MongoDB_hrs, dtype: float64 313    11.0
327    11.0
79     11.0
181    12.0
1      12.0
Name: Data Wrangling with MongoDB_lesson, dtype: float64 98     1.0
279    1.0
280    1.0
282    1.0
91     1.0
Name: Data Wrangling with MongoDB_proj, dtype: float64

we got the progress of the top students according to: the hours studied, the lessons completed, and the completed projects for 2014

In [ ]:
 
In [ ]:
 
In [ ]:
 
In [ ]: